Statistical Computing

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STATYSTYKA OBLICZENIOWA

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Statystyka obliczeniowa

A Visual Guide to Stata Graphics Advanced Markov Chain Monte Carlo
Methods
Michael N. Mitchell Faming Liang
Taylor & Francis Wiley

9781597181068 9780470748268
07.02.2012 16.07.2010
Oprawa: miękka Oprawa: twarda
£ 57,99 £ 67,50
Whether you are new to Stata graphics or a seasoned veteran, A Visual Guide to Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in
Stata Graphics, Third Edition will reach you how to use Stata to make publication- scientific computing. This book discusses recent developments of MCMC methods
quality graphics that will stand out and enhance your statistical results. With over with an emphasis on those making use of past sample information during
900 illustrated examples and quick-reference tabs, this book quickly guides you to simulations. The application examples are drawn from diverse fields such as
the information you need for creating and customizing high-quality graphs for any bioinformatics, machine learning, social science, combinatorial optimization, and
type of statistical data. Each graph is displayed in full color with simple and clear computational physics. Key Features: Expanded coverage of the stochastic
instructions that illustrate how to create and customize graphs using either Stata approximation Monte Carlo and dynamic weighting algorithms that are essentially
commands or the Stata Graph Editor. Stata's powerful graphics system gives you immune to local trap problems. A detailed discussion of the Monte Carlo Metropolis-
complete control over how the elements of your graph look, from marker symbols to Hastings algorithm that can be used for sampling from distributions with intractable
lines, from legends to captions and titles, from axis labels to grid lines, and more. normalizing constants. Up-to-date accounts of recent developments of the Gibbs
Whether you use this book as a learning tool or a quick reference, you will have the sampler. Comprehensive overviews of the population-based MCMC algorithms and
power of Stata graphics at your fingertips. The third edition has been updated and the MCMC algorithms with adaptive proposals. This book can be used as a
expanded to reflect new Stat graphics features, and includes many additional textbook or a reference book for a one-semester graduate course in statistics,
examples. This updated edition illustrates new features to specify fonts and computational biology, engineering, and computer sciences. Applied or theoretical
symbols. researchers will also find this book beneficial.

An Elementary Introduction to An Introduction to Stata for Health
Statistical Learning Theory Researchers
Sanjeev Kulkarni Svend Juul
Wiley Taylor & Francis

9780470641835 9781597180771
15.07.2011 01.10.2010
Oprawa: twarda Oprawa: miękka
£ 66,50 £ 49,99
A thought-provoking look at statistical learning theory and its role in understanding An Introduction to Stata for Health Researchers, Third Edition systematically covers
human learning and inductive reasoning A joint endeavor from leading researchers in data management, simple description and analysis, and more advanced analyses
the fields of philosophy and electrical engineering, An Elementary Introduction to that are most often used in health research, such as regression models, survival
Statistical Learning Theory is a comprehensive and accessible primer on the rapidly analysis, measurement, and diagnosis. It also describes many graph types as well
evolving fields of statistical pattern recognition and statistical learning theory. as how to modify the appearance of a graph. Throughout the text, the authors
Explaining these areas at a level and in a way that is not often found in other books emphasize the importance of good documentation habits to prevent errors and
on the topic, the authors present the basic theory behind contemporary machine wasted time. They demonstrate the use of strategies and tools for documentation.
learning and uniquely utilize its foundations as a framework for philosophical thinking Robust data sets can be downloaded from the book's website. What's New This
about inductive inference. Promoting the fundamental goal of statistical learning, third edition presents some of the new features in Stata 11, including the new,
knowing what is achievable and what is not, this book demonstrates the value of a flexible syntax for factor variables. It also incorporates Stata 11 in the rewritten
systematic methodology when used along with the needed techniques for chapters on regression and survival analysis. Taking into account the improved
evaluating the performance of a learning system. First, an introduction to machine availability of online documentation, this edition points to further reading in the online
learning is presented that includes brief discussions of applications such as image manuals.
recognition, speech recognition, medical diagnostics, and statistical arbitrage.

An R Companion to Linear Statistical Analysis of Questionnaire Data with R
Models
Christopher Hay-Jahans Bruno Falissard
Taylor & Francis Taylor & Francis

9781439873656 9781439817667
20.10.2011 26.09.2011
Oprawa: twarda Oprawa: twarda
£ 52,99 £ 59,99
Focusing on user-developed programming, An R Companion to Linear Statistical While theoretical statistics relies primarily on mathematics and hypothetical
Models serves two audiences: those who are familiar with the theory and situations, statistical practice is a translation of a question formulated by a
applications of linear statistical models and wish to learn or enhance their skills in R; researcher into a series of variables linked by a statistical tool. As with written
and those who are enrolled in an R-based course on regression and analysis of material, there are almost always differences between the meaning of the original
variance. For those who have never used R, the book begins with a self-contained text and translated text. Additionally, many versions can be suggested, each with
introduction to R that lays the foundation for later chapters. their advantages and disadvantages. Analysis of Questionnaire Data with R
This book includes extensive and carefully explained examples of how to write translates certain classic research questions into statistical formulations. As
programs using the R programming language. These examples cover methods used indicated in the title, the syntax of these statistical formulations is based on the well-
for linear regression and designed experiments with up to two fixed-effects factors, known R language, chosen for its popularity, simplicity, and power of its structure.
including blocking variables and covariates. It also demonstrates applications of Although syntax is vital, understanding the semantics is the real challenge of any
several pre-packaged functions for complex computational procedures. good translation. In this book, the semantics of theoretical-to-practical translation
emerges progressively from examples and experience, and occasionally from
mathematical considerations. Sometimes the interpretation of a result is not clear,
and there is no statistical tool really suited to the question at hand. Sometimes data
sets contain errors, inconsistencies between answers, or missing data.

2 www.abe.pl


Applied Medical Statistics Using SAS Applied Reliability

Geoff Der David C. Trindade

9781439867976 9781584884668
30.10.2012 26.08.2011
£ 57,99 £ 63,99
Written with medical statisticians and medical researchers in mind, this intermediate-
Since the publication of the second edition of Applied Reliability in 1995, the ready
level reference explores the use of SAS for analyzing medical data. Applied Medical availability of inexpensive, powerful statistical software has changed the way
Statistics Using SAS covers the whole range of modern statistical methods used in statisticians and engineers look at and analyze all kinds of data. Problems in
the analysis of medical data, including regression, analysis of variance and reliability that were once difficult and time consuming even for experts can now be
covariance, longitudinal and survival data analysis, missing data, generalized additive
solved with a few well-chosen clicks of a mouse. However, software documentation
models (GAMs), and Bayesian methods. The book focuses on performing these has had difficulty keeping up with the enhanced functionality added to new releases,
analyses using SAS, the software package of choice for those analysing medical especially in specialized areas such as reliability analysis. Using analysis capabilities
data. in spreadsheet software and two well-maintained, supported, and frequently
Features updated, popular software packages-Minitab and SAS JMP-the third edition of
 Covers the planning stage of medical studies in detail; several chapters contain Applied Reliability is properties of lifetime distributions such as the exponential,
concepts, and the
an easy-to-use guide to basic descriptive statistics, reliability
details of sample size estimation
Weibull, and lognormal. The material covers reliability data plotting, acceleration
 Illustrates methods of randomisation that might be employed for clinical trials models, life test data analysis, systems models, and much more. The third edition
 Covers topics that have become of great importance in the 21st century, includes a new chapter on Bayesian reliability analysis and expanded, updated
including Bayesian methods and multiple imputation coverage of repairable system modeling.

Applied Survey Data Analysis Bayesian Computation with R

Steven G. Heeringa Jim Albert
Taylor & Francis Springer

9781420080667 9780387922973
07.04.2010 01.06.2009
£ 52,99 € 44,95
Taking a practical approach that draws on the authors’ extensive teaching, There has been a dramatic growth in the development and application of Bayesian
consulting, and research experiences, Applied Survey Data Analysis provides an inferential methods. Some of this growth is due to the availability of powerful
intermediate-level statistical overview of the analysis of complex sample survey data. simulation-based algorithms to summarize posterior distributions. There has been
It emphasizes methods and worked examples using available software procedures also a growing interest in the use of the system R for statistical analyses. R's open
while reinforcing the principles and theory that underlie those methods. source nature, free availability, and large number of contributor packages have
After introducing a step-by-step process for approaching a survey analysis problem, made R the software of choice for many statisticians in education and industry.
the book presents the fundamental features of complex sample designs and shows Bayesian Computation with R introduces Bayesian modeling by the use of
how to integrate design characteristics into the statistical methods and software for computation using the R language. The early chapters present the basic tenets of
survey estimation and inference. The authors then focus on the methods and Bayesian thinking by use of familiar one and two-parameter inferential problems.
models used in analyzing continuous, categorical, and count-dependent variables; Bayesian computational methods such as Laplace's method, rejection sampling,
event history; and missing data problems. Some of the techniques discussed and the SIR algorithm are illustrated in the context of a random effects model. The
include univariate descriptive and simple bivariate analyses, the linear regression construction and implementation of Markov Chain Monte Carlo (MCMC) methods is
model, generalized linear regression modeling methods, the Cox proportional introduced. These simulation-based algorithms are implemented for a variety of
hazards model, discrete time models, and the multiple imputation analysis method. Bayesian applications such as normal and binary response regression, hierarchical
modeling, order-restricted inference, and robust modeling.

Bayesian Programming Computational and Statistical Methods for
Protein Quantification by Mass Spectrometry
Pierre Bessiere Ingvar Eidhammer

9781439880326 9781119964001
06.10.2013 04.01.2013
£ 57,99 £ 60,00
To create a complete computing Bayesian framework, a new modeling The definitive introduction to data analysis in quantitative proteomics This book
methodology is needed to build probabilistic models; new inference algorithms are provides all the necessary knowledge about mass spectrometry based proteomics
required to automate probabilistic calculus; and new programming languages are methods and computational and statistical approaches to pursue the planning,
sought to implement these models on computers; eventually, new hardware will also design and analysis of quantitative proteomics experiments. The author's carefully
be necessary to run these Bayesian programs efficiently. This book describes the constructed approach allows readers to easily make the transition into the field of
current first steps toward the ultimate goal of creating a Bayesian computer. Written quantitative proteomics. Through detailed descriptions of wet-lab methods,
by leading researchers, the text focuses on Bayesian programming and requires computational approaches and statistical tools, this book covers the full scope of a
only basic knowledge of mathematics, making it accessible to non-experts. It quantitative experiment, allowing readers to acquire new knowledge as well as
presents applications from various areas such as robotics. acting as a useful reference work for more advanced readers. Computational and
Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the
use of mass spectrometry in protein quantification and how the bioinformatics
challenges in this field can be solved using statistical methods and various software
programs. Is illustrated by a large number of figures and examples as well as
numerous exercises. Provides both clear and rigorous descriptions of methods and
approaches.

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Computational Statistics Data Analysis and Graphics Using R: An
Example-Based Approach
Geof H. Givens John Maindonald
Wiley Cambridge University Press

9780470533314 9780521762939
07.12.2012 06.05.2010
£ 83,50 £ 50,00
This new edition continues to serve as a comprehensive guide to modern and Discover what you can do with R! Introducing the R system, covering standard
classical methods of statistical computing. The book is comprised of four main regression methods, then tackling more advanced topics, this book guides users
parts spanning the field: through the practical, powerful tools that the R system provides. The emphasis is on
 Optimization hands-on analysis, graphical display, and interpretation of data. The many worked
examples, from real-world research, are accompanied by commentary on what is
 Integration and Simulation done and why. The companion website has code and datasets, allowing readers to
 Bootstrapping reproduce all analyses, along with solutions to selected exercises and updates.
 Density Estimation and Smoothing Assuming basic statistical knowledge and some experience with data analysis (but
Within these sections,each chapter includes a comprehensive introduction and step not R), the book is ideal for research scientists, final-year undergraduate or graduate
-by-step implementation summaries to accompany the explanations of key -level students of applied statistics, and practising statisticians. It is both for learning
methods. The new edition includes updated coverage and existing topics as well as and for reference. This third edition expands upon topics such as Bayesian
new topics such as adaptive MCMC and bootstrapping for correlated data. The inference for regression, errors in variables, generalized linear mixed models, and
book website now includes comprehensive R code for the entire book. There are random forests.
extensive exercises, real examples, and helpful insights about how to use the
methods in practice.

Data Management Using Stata: A Data Manipulation with R
Practical Handbook
Michael N. Mitchell Phil Spector

9781597180764 9780387747309
08.07.2010 07.04.2008
Oprawa: miękka Oprawa: miękka
£ 44,99 € 59,95
Using simple language and illustrative examples, this book comprehensively covers This book presents a wide array of methods applicable for reading data into R, and
data management tasks that bridge the gap between raw data and statistical efficiently manipulating that data. In addition to the built-in functions, a number of
analysis. Rather than focus on clusters of commands, the author takes a modular readily available packages from CRAN (the Comprehensive R Archive Network) are
approach that enables readers to quickly identify and implement the necessary task also covered. All of the methods presented take advantage of the core features of
without having to access background information first. Each section in the chapters R: vectorization, efficient use of subscripting, and the proper use of the varied
presents a self-contained lesson that illustrates a particular data management task functions in R that are provided for common data management tasks. Most
via examples, such as creating data variables and automating error checking. The experienced R users discover that, especially when working with large data sets, it
text also discusses common pitfalls and how to avoid them and provides strategic may be helpful to use other programs, notably databases, in conjunction with R.
data management advice. Ideal for both beginning statisticians and experienced Accordingly, the use of databases in R is covered in detail, along with methods for
users, this handy book helps readers solve problems and learn comprehensive data extracting data from spreadsheets and datasets created by other programs.
management skills. Character manipulation, while sometimes overlooked within R, is also covered in
detail, allowing problems that are traditionally solved by scripting languages to be
carried out entirely within R. For users with experience in other languages, guidelines
for the effective use of programming constructs like loops are provided.

Data Mining with Rattle and R Dynamic Prediction in Clinical Survival
Analysis
Graham Williams Hans van Houwelingen
Springer Taylor & Francis

9781441998897 9781439835333
25.02.2011 10.11.2011
€ 54,95 £ 59,99
Data mining is the art and science of intelligent data analysis. By building knowledge There is a huge amount of literature on statistical models for the prediction of
from information, data mining adds considerable value to the ever increasing stores survival after diagnosis of a wide range of diseases like cancer, cardiovascular
of electronic data that abound today. In performing data mining many decisions disease, and chronic kidney disease. Current practice is to use prediction models
need to be made regarding the choice of methodology, the choice of data, the based on the Cox proportional hazards model and to present those as static models
choice of tools, and the choice of algorithms. Throughout this book the reader is for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in
introduced to the basic concepts and some of the more popular algorithms of data Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at
mining. With a focus on the hands-on end-to-end process for data mining, Williams later points in time, for instance using landmark models. Designed to be useful to
guides the reader through various capabilities of the easy to use, free, and open applied statisticians and clinical epidemiologists, each chapter in the book has a
source Rattle Data Mining Software built on the sophisticated R Statistical Software. practical focus on the issues of working with real life data. Chapters conclude with
The focus on doing data mining rather than just reading about data mining is additional material either on the interpretation of the models, alternative models, or
refreshing. This book covers data understanding, data preparation, data refinement, theoretical background.
model building, model evaluation, and practical deployment. The reader will learn to
rapidly deliver a data mining project using software easily installed for free from the
Internet. "Coupling Rattle with R" delivers a very sophisticated data mining
environment with all the power, and more, of the many commercial offerings.

4 www.abe.pl


Equivalence and Noninferiority Tests for Flexible Parametric Survival Analysis
Quality, Manufacturing and Test Engineers Using Stata
Scott Pardo Patrick Royston

9781466586888 9781597180795
15.09.2013 15.08.2011
£ 82,00 £ 49,99
This work provides readers with a set of statistical methods useful for demonstrating Through real-world case studies, this book shows how to use Stata to estimate a
equivalence or noninferiority of new or revised systems either compared to class of flexible parametric survival models. It discusses the modeling of time-
previously existing systems or previously established performance guidelines. These dependent and continuous covariates and looks at how relative survival can be used
tests are often performed in the context of process or product validation. The author to measure mortality associated with a particular disease when the cause of death
describes all the necessary calculations, which can be made using software such as has not been recorded. The book describes simple quantification of differences
Minitab or JMP. Fully worked examples are provided for each method. between any two covariate patterns through calculation of time-dependent hazard
ratios, hazard differences, and survival differences.

Foundations of Interconnection Ggplot2
Networks
A.Yavuz Oruc Hadley Wickham

9781466559424 9780387981406
05.12.2013 07.08.2009
£ 57,99 € 52,95
Suitable for researchers and graduate students in computer science, electrical This book describes ggplot2, a new data visualization package for R that uses the
engineering, and applied mathematics, this book presents a concise yet complete insights from Leland Wilkison's Grammar of Graphics to create a powerful and
account of the most significant results in interconnection network theory. The flexible system for creating data graphics. With ggplot2, it's easy to:
authors give a mathematical classification and characterization of the key
interconnection functions of networks and establish their switching factor
 produce handsome, publication-quality plots, with automatic legends created
from the plot specification
complexities using combinatorial methods. The text leads readers through the
historical development, covering major contributions from Shannon, Clos, Benes,  superpose multiple layers (points, lines, maps, tiles, box plots to name a few)
Cantor, Pinsker, and Margulis. It also explores recent results, such as quantum from different data sources, with automatically adjusted common scales
switching networks.  add customisable smoothers that use the powerful modelling capabilities of R,
such as loess, linear models, generalised additive models and robust regression
 save any ggplot2 plot (or part thereof) for later modification or reuse
 create custom themes that capture in-house or journal style requirements, and
that can easily be applied to multiple plots

Guidebook to R Graphics Using Handbook of Partial Least Squares:
Microsoft Windows Concepts, Methods and Applications in
Kunio Takezawa Vincenzo Esposito Vinzi
Wiley Springer

9781118026397 9783540328254
30.03.2012 30.05.2007
£ 46,95 € 286
This book introduces the graphical capabilities of R to readers new to the software, This handbook provides a comprehensive overview of Partial Least Squares (PLS)
taking readers step by step through the process of creating histograms, boxplots, methods with specific reference to their use in marketing and with a discussion of
strip charts, time series graphs, steam-and-leaf displays, scatterplot matrices, and the directions of current research and perspectives. It covers the broad area of PLS
map graphs. Throughout the book, concise explanations of key concepts of R methods, from regression to structural equation modeling applications, software and
graphics assist readers in carrying out the presented procedures. The discussed interpretation of results. The handbook serves both as an introduction for those
techniques are accompanied by a wealth of screenshots and graphics with related without prior knowledge of PLS and as a comprehensive reference for researchers
R code available on the book's FTP site, and numerous exercises allow readers to and practitioners interested in the most recent advances in PLS methodology.
test their understanding of the presented material.

www.abe.pl 5


Handbook of SAS Data Step Image Statistics and Computer
Programming Graphics
Arthur Li Tania Pouli

9781466552388 9781568817255
10.05.2013 06.11.2013
£ 38,99 £ 44,99
To write an accomplished program in the DATA step of SAS®, programmers must The statistics of natural images have attracted the attention of researchers in a
understand programming logic and know how to implement and even create their variety of fields as a means to better understand the human visual system and its
own programming algorithm. Handbook of SAS® DATA Step Programming shows processes. A number of algorithms in computer graphics and vision and image
readers how best to manage and manipulate data by using the DATA step. processing take advantage of such statistical findings to create visually more
The book helps novices avoid common mistakes resulting from a lack of plausible results. This book explores the state of the art in image statistics and
understanding fundamental and unique SAS programming concepts. It explains that discusses existing and potential applications within computer graphics and related
learning syntax does not solve all problems; rather, a thorough comprehension of areas.
SAS processing is needed for successful programming. The author also guides
readers through a programming task. In most of the examples, the author first
presents strategies and steps for solving the problem, then offers a solution, and
finally gives a more detailed explanation of the solution.
Understanding the DATA steps, particularly the program data vector (PDV), is critical
to proper data manipulation and management in SAS. This book helps SAS
programmers thoroughly grasp the concept of DATA step processing and write
accurate programs in the DATA step.

Industrial Statistics with Minitab Introducing Monte Carlo Methods with
R
Pere Grima Cintas Christian P. Robert (INSEE, Malakoff, France)
Wiley Springer

9780470972755 9781441915757
14.09.2012 07.12.2009
£ 55,00 € 54,95
Industrial Statistics with MINITAB demonstrates the use of MINITAB as a tool for Computational techniques based on simulation have now become an essential part
performing statistical analysis in an industrial context. This book covers introductory of the statistician's toolbox. It is thus crucial to provide statisticians with a practical
industrial statistics, exploring the most commonly used techniques alongside those understanding of those methods, and there is no better way to develop intuition and
that serve to give an overview of more complex issues. A plethora of examples in skills for simulation than to use simulation to solve statistical problems. Introducing
MINITAB are featured along with case studies for each of the statistical techniques Monte Carlo Methods with R covers the main tools used in statistical simulation
presented. Industrial Statistics with MINITAB : Provides comprehensive coverage of from a programmer's point of view, explaining the R implementation of each
user-friendly practical guidance to the essential statistical methods applied in simulation technique and providing the output for better understanding and
industry. Explores statistical techniques and how they can be used effectively with comparison. While this book constitutes a comprehensive treatment of simulation
the help of MINITAB 16. Contains extensive illustrative examples and case studies methods, the theoretical justification of those methods has been considerably
throughout and assumes no previous statistical knowledge. Emphasises data reduced, compared with Robert and Casella (2004). Similarly, the more exploratory
graphics and visualization, and the most used industrial statistical tools, such as and less stable solutions are not covered here. This book does not require a
Statistical Process Control and Design of Experiments. Is supported by an preliminary exposure to the R programming language or to Monte Carlo methods,
accompanying website featuring case studies and the corresponding datasets. Six nor an advanced mathematical background. While many examples are set within a
Sigma Green Belts and Black Belts will find explanations and examples of the most Bayesian framework, advanced expertise in Bayesian statistics is not required.
relevant techniques in DMAIC projects.

Introduction to Digital Systems: Large-Scale Inverse Problems and
Modeling, Synthesis, and Simulation Quantification of Uncertainty
Mohammed Ferdjallah Lorenz T. Biegler
Wiley Wiley

9780470900550 9780470697436
22.07.2011 05.11.2010
£ 76,95 £ 79,95
A unique guide to using both modeling and simulation in digital systems design This book focuses on computational methods for large-scale statistical inverse
Digital systems design requires rigorous modeling and simulation analysis that problems and provides an introduction to statistical Bayesian and frequentist
eliminates design risks and potential harm to users. Introduction to Digital Systems: methodologies. Recent research advances for approximation methods are
Modeling, Synthesis, and Simulation Using VHDL introduces the application of discussed, along with Kalman filtering methods and optimization-based approaches
modeling and synthesis in the effective design of digital systems and explains to solving inverse problems. The aim is to cross-fertilize the perspectives of
applicable analytical and computational methods. Through step-by-step researchers in the areas of data assimilation, statistics, large-scale optimization,
explanations and numerous examples, the author equips readers with the tools applied and computational mathematics, high performance computing, and cutting-
needed to model, synthesize, and simulate digital principles using Very High Speed edge applications. The solution to large-scale inverse problems critically depends on
Integrated Circuit Hardware Description Language (VHDL) programming. methods to reduce computational cost. Recent research approaches tackle this
Extensively classroom-tested to ensure a fluid presentation, this book provides a challenge in a variety of different ways. Many of the computational frameworks
comprehensive overview of the topic by integrating theoretical principles, discrete highlighted in this book build upon state-of-the-art methods for simulation of the
mathematical models, computer simulations, and basic methods of analysis. forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-
order models and emulators of the forward problem, stochastic spectral
approximations, and ensemble-based approximations, as well as exploiting the
machinery for large-scale deterministic optimization through adjoint and other ...

6 www.abe.pl


Making Sense of Data Set Maximum Likelihood Estimation with
Stata
Glenn J. Myatt William Gould

9781118395141 9781597180788
30.03.2012 15.11.2010
Oprawa: miękka Oprawa: miękka
£ 153,00 £ 49,99
Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers
in all disciplines who need to compute maximum likelihood estimators that are not
available as prepackaged routines. Readers are presumed to be familiar with Stata,
but no special programming skills are assumed except in the last few chapters,
which detail how to add a new estimation command to Stata. The book begins with
an introduction to the theory of maximum likelihood estimation with particular
attention on the practical implications for applied work. Individual chapters then
describe in detail each of the four types of likelihood evaluator programs and provide
numerous examples, such as logit and probit regression, Weibull regression,
random-effects linear regression, and the Cox proportional hazards model. Later
chapters and appendixes provide additional details about the ml command, provide
checklists to follow when writing evaluators, and show how to write your own
estimation commands.

Methods of Statistical Model Estimation Modeling and Simulation
Fundamentals: Theoretical
Joseph M. Hilbe John A. Sokolowski

9781439858028 9780470486740
08.07.2013 14.05.2010
£ 57,99 £ 76,50
Methods of Statistical Model Estimation provides readers with an examination of the An insightful presentation of the key concepts, paradigms, and applications of
major methods used by researchers and programmers to estimate statistical model modeling and simulation Modeling and simulation has become an integral part of
parameters and associated statistics. Designed for R programmers, the book is also research and development across many fields of study, having evolved from a tool
suitable for anyone wanting to better understand the optimization algorithms used to a discipline in less than two decades. Modeling and Simulation Fundamentals
for model estimation. The text focuses on R programming code for the estimation of offers a comprehensive and authoritative treatment of the topic and includes
a variety of regression procedures using maximum likelihood estimation, iteratively definitions, paradigms, and applications to equip readers with the skills needed to
reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully work successfully as developers and users of modeling and simulation. Featuring
developed code is constructed in the book for each of the discussed methods of contributions written by leading experts in the field, the book's fluid presentation
estimation, including working code for OLS regression, a near complete generalized builds from topic to topic and provides the foundation and theoretical underpinnings
linear models function, one- and two-parameter maximum likelihood models for of modeling and simulation. First, an introduction to the topic is presented, including
both pooled and panel models, a random effects model estimated using the EM related terminology, examples of model development, and various domains of
algorithm, and a Bayesian Poisson model using Metropolis-Hastings sampling. The modeling and simulation.
authors also discuss a number of ancillary issues.

Modern Signal Processing Multivariate Survival and Competing
Risks
Daniel N. Rockmore Martin J. Crowder
Cambridge University Press Taylor & Francis

9780521158213 9781439875216
15.07.2010 17.05.2012
£ 32,99 £ 63,99
Signal processing is everywhere in modern technology. Its mathematical basis and Multivariate Survival Analysis and Competing Risks introduces univariate survival
many areas of application are the subject of this 2004 book, based on a series of analysis and extends it to the multivariate case. It covers competing risks and
graduate-level lectures held at the Mathematical Sciences Research Institute. counting processes and provides many real-world examples, exercises, and R
Emphasis is on challenges in the subject, particular techniques adapted to particular code. The text discusses survival data, survival distributions, frailty models,
technologies, and certain advances in algorithms and theory. The book covers two parametric methods, multivariate data and distributions, copulas, continuous failure,
main areas: computational harmonic analysis, envisioned as a technology for parametric likelihood inference, and non- and semi-parametric methods. There are
efficiently analysing real data using inherent symmetries; and the challenges inherent many books covering survival analysis, but very few that cover the multivariate case
in the acquisition, processing and analysis of images and sensing data in general in any depth. Written for a graduate-level audience in statistics/biostatistics, this
[EMDASH] ranging from sonar on a submarine to a neuroscientist's fMRI study. book includes practical exercises and R code for the examples. The author is
renowned for his clear writing style, and this book continues that trend. It is an
excellent reference for graduate students and researchers looking for grounding in
this burgeoning field of research.

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Network and Adaptive Sampling Numerical Methods of Statistics
Techniques
Arijit Chaudhuri John F. Monahan
Taylor & Francis Cambridge University Press

9781466577565 9780521139519
22.06.2013 18.04.2011
£ 49,99 £ 36,99
Combining the two statistical techniques of network sampling and adaptive This book explains how computer software is designed to perform the tasks
sampling, this book illustrates the advantages of using them in tandem to effectively required for sophisticated statistical analysis. For statisticians, it examines the nitty-
capture sparsely located elements in unknown pockets. It shows how network gritty computational problems behind statistical methods. For mathematicians and
sampling is a reliable guide in capturing inaccessible entities through linked computer scientists, it looks at the application of mathematical tools to statistical
auxiliaries. The text also explores how adaptive sampling is strengthened in problems. The first half of the book offers a basic background in numerical analysis
information content through subsidiary sampling with devices to mitigate that emphasizes issues important to statisticians. The next several chapters cover a
unmanageable expanding sample sizes. Empirical data illustrates the applicability of broad array of statistical tools, such as maximum likelihood and nonlinear
both methods. regression. The author also treats the application of numerical tools; numerical
integration and random number generation are explained in a unified manner
reflecting complementary views of Monte Carlo methods. Each chapter contains
exercises that range from simple questions to research problems. Most of the
examples are accompanied by demonstration and source code available from the
author's website. New in this second edition are demonstrations coded in R, as well
as new sections on linear programming and the Nelder-Mead search algorithm.

Practical Statistical Methods: A SAS Probability and Statistics for Computer
Programming Approach Scientists
Lakshmi Padgett Michael Baron

9781439812822 9781439875902
25.04.2011 15.08.2013
£ 52,99 £ 63,99
Practical Statistical Methods: A SAS Programming Approach presents a broad Presenting probability and statistical methods, simulation techniques, and modeling
spectrum of statistical methods useful for researchers without an extensive tools, this book helps students solve problems and make optimal decisions in
statistical background. In addition to nonparametric methods, it covers methods for uncertain conditions, select stochastic models, compute probabilities and forecasts,
discrete and continuous data. Omitting mathematical details and complicated and evaluate performance of computer systems and networks. It covers how to
formulae, the text provides SAS programs to carry out the necessary analyses and read a word problem or a corporate report, realize the uncertainty involved in the
draw appropriate inferences for common statistical problems. After introducing described situation, select a suitable probability model, estimate and test its
fundamental statistical concepts, the author describes methods used for parameters based on real data, compute probabilities, and make appropriate
quantitative data and continuous data following normal and nonnormal distributions. conclusions. This edition features over 100 pages of new material covering
She then focuses on regression methodology, highlighting simple linear regression, categorical data analysis, nonparametric tests, and regression diagnostics.
logistic regression, and the proportional hazards model. The final chapter briefly
discusses such miscellaneous topics as propensity scores, misclassification errors,
interim analysis, conditional power, bootstrap, and jackknife. With SAS code and
output integrated throughout, this book shows how to interpret data using SAS and
illustrates the many statistical methods available for tackling problems in a range of
fields, including the pharmaceutical industry and the social sciences.

Quasi-Least Squares Regression R for SAS and SPSS Users

Justine Shults Robert A. Muenchen

9781420099935 9781461406846
06.12.2013 23.07.2011
£ 57,99 € 89,95
Drawing on the authors’ substantial expertise in modeling longitudinal and clustered R is a powerful and free software system for data analysis and graphics, with over
data, this book presents a comprehensive treatment of quasi-least squares (QLS) 1,200 add-on packages available. This book introduces R using SAS and SPSS
regression—a computational approach for the estimation of correlation parameters terms with which you are already familiar. It demonstrates which of the add-on
within the framework of generalized estimating equations (GEEs). The authors packages are most like SAS and SPSS and compares them to R's built-in functions.
present an overview and detailed evaluation of QLS methodology, demonstrating It steps through over 30 programs written in all three packages, comparing and
the advantages of QLS in comparison with alternative methods. They describe how contrasting the packages' differing approaches. The programs and practice
QLS can be used to extend the application of the traditional GEE approach to the datasets are available for download. The glossary defines over 50 R terms using
analysis of unequally spaced longitudinal data, familial data, and data with multiple SAS/SPSS jargon and again using R jargon. The table of contents and the index
sources of correlation. In some settings, QLS also allows for improved analysis with allow you to find equivalent R functions by looking up both SAS statements and
an unstructured correlation matrix. SPSS commands. When finished, you will be able to import data, manage and
Special focus is given to goodness-of-fit analysis as well as new strategies for transform it, create publication quality graphics, and perform basic statistical
selecting the appropriate working correlation structure for QLS and GEE. analyses. This new edition has updated programming, an expanded index, and even
more statistical methods covered in over 25 new sections.

8 www.abe.pl

Statystyka komputerowa

R for Statistics R Graphics

Pierre-Andre Cornillon Paul Murrell

9781439881453 9781439831762
17.04.2012 07.06.2011
£ 34,99 £ 52,99
Although there are currently a wide variety of software packages suitable for the Extensively updated to reflect the evolution of statistics and computing, the second
modern statistician, R has the triple advantage of being comprehensive, edition of the bestselling R Graphics comes complete with new packages and new
widespread, and free. Published in 2008, the second edition of Statistiques avec R examples. Paul Murrell, widely known as the leading expert on R graphics, has
enjoyed great success as an R guidebook in the French-speaking world. Translated developed an in-depth resource that helps both neophyte and seasoned users
and updated, R for Statistics includes a number of expanded and additional worked master the intricacies of R graphics. New in the Second Edition Updated information
examples. Organized into two sections, the book focuses first on the R software, on the core graphics engine, the traditional graphics system, the grid graphics
then on the implementation of traditional statistical methods with R. Focusing on the system, and the lattice package A new chapter on the ggplot2 package New
R software, the first section covers: Basic elements of the R software and data chapters on applications and extensions of R Graphics, including geographic maps,
processing Clear, concise visualization of results, using simple and complex graphs dynamic and interactive graphics, and node-and-edge graphs Organized into five
Programming basics: pre-defined and user-created functions The second section of parts, R Graphics covers both "traditional" and newer, R-specific graphics systems.
the book presents R methods for a wide range of traditional statistical data The book reviews the graphics facilities of the R language and describes R's
processing techniques, including: Regression methods Analyses of variance and powerful grid graphics system. It then covers the graphics engine, which represents
covariance Classification methods Exploratory multivariate analysis Clustering a common set of fundamental graphics facilities, and provides a series of brief
methods Hypothesis tests After a short presentation of the method, the book overviews of the major areas of application for R graphics and the major extensions
explicitly details the R command lines and gives commented results. of R graphics.

Solving Differential Equations in R Spatial Analysis Along Networks

Karline Soetaert Atsuyuki Okabe
Springer Wiley

9783642280696 9780470770818
07.06.2012 27.07.2012
€ 49,95 £ 65,00
Mathematics plays an important role in many scientific and engineering disciplines. In the real world, there are numerous and various events that occur on and
This book deals with the numerical solution of differential equations, a very important alongside networks, including the occurrence of traffic accidents on highways, the
branch of mathematics. Our aim is to give a practical and theoretical account of how location of stores alongside roads, the incidence of crime on streets and the
to solve a large variety of differential equations, comprising ordinary differential contamination along rivers. In order to carry out analyses of those events, the
equations, initial value problems and boundary value problems, differential algebraic researcher needs to be familiar with a range of specific techniques. Spatial Analysis
equations, partial differential equations and delay differential equations. The solution Along Networks provides a practical guide to the necessary statistical techniques
of differential equations using R is the main focus of this book. It is therefore and their computational implementation. Each chapter illustrates a specific
intended for the practitioner, the student and the scientist, who wants to know how technique, from Stochastic Point Processes on a Network and Network Voronoi
to use R for solving differential equations. However, it has been our goal that non- Diagrams, to Network K-function and Point Density Estimation Methods, and the
mathematicians should at least understand the basics of the methods, while Network Huff Model. The authors also discuss and illustrate the undertaking of the
obtaining entrance into the relevant literature that provides more mathematical statistical tests described in a Geographical Information System (GIS) environment
background. Therefore, each chapter that deals with R examples is preceded by a as well as demonstrating the user-friendly free software package SANET.
chapter where the theory behind the numerical methods being used is introduced.

Statistical Data Mining Using SAS Statistical Inference
Applications
George Fernandez Murray Aitkin

9781439810750 9781420093438
29.06.2010 01.06.2010
£ 62,99 £ 62,99
Statistical Data Mining Using SAS Applications, Second Edition describes statistical This book sets out an integrated approach to statistical inference using the
data mining concepts and demonstrates the features of user-friendly data mining likelihood function as the primary measure of evidence for statistical model
SAS tools. Integrating the statistical and graphical analysis tools available in SAS parameters, and for the statistical models themselves. The author provides both an
systems, the book provides complete statistical data mining solutions without alternative to standard Bayesian inference and the foundation for a course sequence
writing SAS program codes or using the point-and-click approach. Each chapter in modern Bayesian theory at the graduate or advanced undergraduate level. The
emphasizes step-by-step instructions for using SAS macros and interpreting the restriction of the book to evidence is deliberate: there are already many books on
results. Compiled data mining SAS macro files are available for download on the Bayesian and non-Bayesian decision theory, and the purpose of this one is less
author's website. By following the step-by-step instructions and downloading the ambitious, but perhaps more relevant scientifically, in providing a detailed
SAS macros, analysts can perform complete data mining analysis fast and prescription for the assessment of statistical evidence.
effectively. New to the Second Edition-General Features Access to SAS macros
directly from desktop Compatible with SAS version 9, SAS Enterprise Guide, and
SAS Learning Edition Reorganization of all help files to an appendix Ability to create
publication quality graphics Macro-call error check New Features in These SAS-
Specific Macro Applications Converting PC data files to SAS data (EXLSAS2 macro)
Randomly splitting data (RANSPLIT2) Frequency analysis (FREQ2) Univariate ...

www.abe.pl 9


Statistical Inference: The Minimum Statistical Learning and Data Science
Distance Approach
Ayanendranath Basu Mireille Gettler Summa

9781420099652 9781439867631
16.06.2011 18.01.2012
£ 59,99 £ 59,99
This book gives a comprehensive account of density-based minimum distance Data analysis is changing fast. Driven by a vast range of application domains and
methods and their use in statistical inference. It covers statistical distances, density- affordable tools, machine learning has become mainstream. Unsupervised data
based minimum distance methods, discrete and continuous models, asymptotic analysis, including cluster analysis, factor analysis, and low dimensionality mapping
distributions, robustness, computational issues, residual adjustment functions, methods continually being updated, have reached new heights of achievement in
graphical descriptions of robustness, penalized and combined distances, the incredibly rich data world that we inhabit. Statistical Learning and Data Science
multisample methods, weighted likelihood, and multinomial goodness-of-it tests. is a work of reference in the rapidly evolving context of converging methodologies. It
The book also introduces the minimum distance methodology in interdisciplinary gathers contributions from some of the foundational thinkers in the different fields of
areas, such as neural networks and image processing, as well as specialized data analysis to the major theoretical results in the domain. On the methodological
models and problems, including regression, mixture models, survival and Bayesian front, the volume includes conformal prediction and frameworks for assessing
analysis, and more. confidence in outputs, together with attendant risk. It illustrates a wide range of
applications, including semantics, credit risk, energy production, genomics, and
ecology. The book also addresses issues of origin and evolutions in the
unsupervised data analysis arena, and presents some approaches for time series,
symbolic data, and functional data.

The R Book The R Primer

Michael J. Crawley Claus Thorn Ekstrom

9780470973929 9781439862063
07.12.2012 25.08.2011
£ 60,00 £ 26,99
Hugely successful and popular text presenting an extensive and comprehensive Newcomers to R are often intimidated by the command-line interface, the vast
guide for all R users The R language is recognized as one of the most powerful and number of functions and packages, or the processes of importing data and
flexible statistical software packages, enabling users to apply many statistical performing a simple statistical analysis. The R Primer provides a collection of
techniques that would be impossible without such software to help implement such concise examples and solutions to R problems frequently encountered by new
large data sets. R has become an essential tool for understanding and carrying out users of this statistical software. Rather than explore the many options available for
research. This edition: Features full colour text and extensive graphics throughout. every command as well as the ever-increasing number of packages, the book
Introduces a clear structure with numbered section headings to help readers locate focuses on the basics of data preparation and analysis and gives examples that can
information more efficiently. Looks at the evolution of R over the past five years. be used as a starting point. The numerous examples illustrate a specific situation,
Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully topic, or problem, including data importing, data management, classical statistical
revised and updated bibliography and reference section. Is supported by an analyses, and high-quality graphics production. Each example is self-contained and
accompanying website allowing examples from the text to be run by the user. Praise includes R code that can be run exactly as shown, enabling results from the book to
for the first edition: '...if you are an R user or wannabe R user, this text is the one be replicated. While base R is used throughout, other functions or packages are
that should be on your shelf. listed if they cover or extend the functionality. After working through the examples
found in this text, new users of R will be able to better handle data analysis and
graphics applications in R.

The R Student Companion Transforms and Applications Primer for
Engineers with Examples and MATLAB
Brian Dennis Alexander D. Poularikas

9781439875407 9781420089318
10.10.2012 09.03.2010
£ 25,99 £ 48,99
R is the amazing, free, open-access software package for scientific graphs and Transforms and Applications Primer for Engineers with Examples and MATLAB
calculations used by scientists worldwide. The R Student Companion is a student- [registered] is required reading for engineering and science students, professionals,
oriented manual describing how to use R in high school and college science and and anyone working on problems involving transforms. This invaluable primer
mathematics courses. Written for beginners in scientific computation, the book contains the most essential integral transforms that both practicing engineers and
assumes the reader has just some high school algebra and has no computer students need to understand. It provides a large number of examples to explain the
programming background. The author presents applications drawn from all sciences use of transforms in different areas, including circuit analysis, differential equations,
and social sciences and includes the most often used features of R in an appendix. signals and systems, and mechanical vibrations. It includes an appendix with
In addition, each chapter provides a set of computational challenges: exercises in R suggestions and explanations to help you optimize your use of MATLAB Laplace
calculations that are designed to be performed alone or in groups. Several of the and Fourier transforms are by far the most widely used and most useful of all integral
chapters explore algebra concepts that are highly useful in scientific applications, transforms, so they are given a more extensive treatment in this book, compared to
such as quadratic equations, systems of linear equations, trigonometric functions, other texts that include them. Offering numerous MATLAB functions created by the
and exponential functions. Each chapter provides an instructional review of the author, this comprehensive book contains several appendices to complement the
algebra concept, followed by a hands-on guide to performing calculations and main subjects. Perhaps the most important feature is the extensive tables of
graphing in R. R is intuitive, even fun. Fantastic, publication-quality graphs of data, transforms, which are provided to supplement the learning process.
equations, or both can be produced with little effort.

10 www.abe.pl


Tree-Based Graph Partitioning Understanding Biplots: Methods and
Constraint Applications of Biplots
Xavier Lorca John C. Gower
Wiley Wiley

9781848213036 9780470012550
12.07.2011 24.12.2010
£ 63,50 £ 68,95
Combinatorial problems based on graph partitioning enable us to mathematically Biplots are a graphical method for simultaneously displaying two kinds of
represent and model many practical applications. Mission planning and the routing information; typically, the variables and sample units described by a multivariate data
problems occurring in logistics perfectly illustrate two such examples. Nevertheless, matrix or the items labelling the rows and columns of a two-way table. This book
these problems are not based on the same partitioning pattern: generally, patterns aims to popularize what is now seen to be a useful and reliable method for the
like cycles, paths, or trees are distinguished. Moreover, the practical applications are visualization of multidimensional data associated with, for example, principal
often not limited to theoretical problems like the Hamiltonian path problem, or K- component analysis, canonical variate analysis, multidimensional scaling,
node disjoint path problems. Indeed, they usually combine the graph partitioning multiplicative interaction and various types of correspondence analysis.
problem with several restrictions related to the topology of nodes and arcs. The Understanding Biplots:
diversity of implied constraints in real-life applications is a practical limit to the  Introduces theory and techniques which can be applied to problems from a
resolution of such problems by approaches considering the partitioning problem variety of areas, including ecology, biostatistics, finance, demography and other
independently from each additional restriction. This book focuses on constraint social sciences.
satisfaction problems related to tree partitioning problems enriched by several  Provides novel techniques for the visualization of multidimensional data and
additional constraints that restrict the possible partitions topology. On the one hand, includes data mining techniques.
this title focuses on the structural properties of tree partitioning constraints.  Uses applications from many fields including finance, biostatistics, ecology,
demography.

Understanding Computational Bayesian Using IBM® SPSS® Statistics for Research
Statistics Methods and Social Science Statistics
William M. Bolstad Warner
Wiley SAGE

9780470046098 9781452217703
11.01.2010 02.04.2012
£ 79,50 £ 18,99
A hands-on introduction to computational statistics from a Bayesian point of view Ideal either as a companion to a traditional statistics or research methods text or as
Providing a solid grounding in statistics while uniquely covering the topics from a a stand-alone guide, Using SPSS for Research Methods and Social Statistics is a
Bayesian perspective, Understanding Computational Bayesian Statistics useful reference for those learning to use the SPSS software for the first time or
successfully guides readers through this new, cutting-edge approach. With its those with only basic knowledge about SPSS. This student-friendly resource shows
hands-on treatment of the topic, the book shows how samples can be drawn from readers how to use images and directions drawn from SPSS Version 20 and now
the posterior distribution when the formula giving its shape is all that is known, and uses the latest version of the General Social Survey (GSS, 2010) as a secondary
how Bayesian inferences can be based on these samples from the posterior. These data set.
ideas are illustrated on common statistical models, including the multiple linear
regression model, the hierarchical mean model, the logistic regression model, and
the proportional hazards model. The book begins with an outline of the similarities
and differences between Bayesian and the likelihood approaches to statistics.
Subsequent chapters present key techniques for using computer software to draw
Monte Carlo samples from the incompletely known posterior distribution and
performing the Bayesian inference calculated from these samples.

Visual Data Mining: The VisMiner
Approach
Russell K. Anderson
Wiley

9781119967545
16.11.2012
Oprawa: twarda
£ 45,00
This book introduces a visual methodology for data mining demonstrating the
application of methodology along with a sequence of exercises using VisMiner.
VisMiner has been developed by the author and provides a powerful visual data
mining tool enabling readers to visually evaluate models created from the data. This
book is designed as a hands-on work book to introduce the methodologies to
students in data mining, advanced statistics, and business intelligence courses. It
provides a set of tutorials, exercises, and case studies that support readers in
learning data mining processes.

www.abe.pl 11

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