For more than a quarter of a century, this internationally recognized series has fostered the growth of statistical science by publishing upper level textbooks of high quality at reasonable prices. These texts, which cover new frontiers as well as developments in core areas, continue to have a major role in shaping the discipline through the education of young scientists both in statistics as well as in fields wherein the role of statistics is becoming increasingly important.
The series covers a very broad domain. Students in upper level undergraduate and graduate courses in biostatistics, epidemiology, probability and statistics will constitute the primary readership for the series. However, others in areas such as engineering, life science, business, environmental science and social science will find books of interest. Scientists in these areas will also find useful references since emphasis is placed on readability, real examples and case studies, and on tying theory into relevant software such as SAS, Stata, and R.
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By Christopher R. Bilder, Thomas M. Loughin
July 18, 2024
Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ...
By Peng Ding
June 18, 2024
The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. This textbook, based on the author's course on causal inference at UC Berkeley taught over the ...
By Anne Casella, Roger Berger
May 23, 2024
This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and ...
By Walter W. Stroup, Marina Ptukhina, Julie Garai
May 21, 2024
Generalized Linear Mixed Models: Modern Concepts, Methods and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see ...
By John Kloke, Joseph McKean
May 20, 2024
Praise for the first edition: “This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.” -The American Statistician This thoroughly updated and expanded second edition of Nonparametric Statistical ...
By W. Jackson Hall, David Oakes
December 14, 2023
This book provides an accessible but rigorous introduction to asymptotic theory in parametric statistical models. Asymptotic results for estimation and testing are derived using the “moving alternative” formulation due to R. A. Fisher and L. Le Cam. Later chapters include discussions of linear rank...
By Gavin Shaddick, James V. Zidek, Alexandra M. Schmidt
December 12, 2023
Spatio-Temporal Methods in Environmental Epidemiology with R, like its First Edition, explores the interface between environmental epidemiology and spatio-temporal modeling. It links recent developments in spatio-temporal theory with epidemiological applications. Drawing on real-life problems, it ...
By Per Kragh Andersen, Henrik Ravn
October 11, 2023
Multi-state models provide a statistical framework for studying longitudinal data on subjects when focus is on the occurrence of events that the subjects may experience over time. They find application particularly in biostatistics, medicine, and public health. The book includes mathematical detail...
By Raquel Prado, Marco A. R. Ferreira, Mike West
September 25, 2023
Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and ...
By Sergio Rey, Dani Arribas-Bel, Levi John Wolf
June 14, 2023
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in ...
By David Collett
May 31, 2023
Modelling Survival Data in Medical Research, Fourth Edition, describes the analysis of survival data, illustrated using a wide range of examples from biomedical research. Written in a non-technical style, it concentrates on how the techniques are used in practice. Starting with standard methods for...
By Steffen Lauritzen
April 17, 2023
Fundamentals of Mathematical Statistics is meant for a standard one-semester advanced undergraduate or graduate-level course in Mathematical Statistics. It covers all the key topics—statistical models, linear normal models, exponential families, estimation, asymptotics of maximum likelihood, ...