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**Michael Baron**

June 14, 2019

Praise for the Second Edition: "The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated with...

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**W. Holmes Finch, Jocelyn E. Bolin, Ken Kelley**

May 20, 2019

Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how ...

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**Peter D. Congdon**

September 30, 2019

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. ...

**Michael Panik**

October 07, 2019

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs....

**Peter D. Congdon**

September 30, 2019

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections....

**Jim Albert, Mark E. Glickman, Tim B. Swartz, Ruud H. Koning**

September 27, 2019

This handbook will provide both overviews of statistical methods in sports and in-depth treatment of critical problems and challenges confronting statistical research in sports. The material in the handbook will be organized by major sport (baseball, football, hockey, basketball, and soccer)...

**Murray Aitkin**

September 05, 2019

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to...

**Christian Ritz, Signe Marie Jensen, Daniel Gerhard, Jens Carl Streibig**

July 11, 2019

Nowadays the term dose-response is used in many different contexts and many different scientific disciplines including agriculture, biochemistry, chemistry, environmental sciences, genetics, pharmacology, plant sciences, toxicology, and zoology. In the 1940 and 1950s, dose-response analysis was...

**Silvia Bacci, Bruno Chiandotto**

July 08, 2019

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in...

**Michael Baron**

June 14, 2019

Praise for the Second Edition: "The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated...

**Hadley Wickham**

May 30, 2019

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the...

**Jana Jurečková, Jan Picek, Martin Schindler**

May 23, 2019

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment, and the updated chapter on the multivariate model...

**Shu Kay Ng, Liming Xiang, Kelvin Kai Wing Yau**

May 21, 2019

Mixture Modelling for Medical and Health Sciences provides a direct connection between theoretical developments in mixture modelling and their applications in real world problems. The book describes the development of the most important concepts through comprehensive analyses of real and practical...

**W. Holmes Finch, Jocelyn E. Bolin, Ken Kelley**

May 20, 2019

Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how...

**Elizabeth Ann Maharaj, Pierpaolo D'Urso, Jorge Caiado**

April 12, 2019

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and...