Chapman & Hall/CRC Texts in Statistical Science
About the Book Series
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.
Please contact us if you have an idea for a book for the series.
Principles of Uncertainty
2nd Edition
By Joseph B. Kadane
May 27, 2024
Praise for the first edition: Principles of Uncertainty is a profound and mesmerising book on the foundations and principles of subjectivist or behaviouristic Bayesian analysis. … the book is a pleasure to read. And highly recommended for teaching as it can be used at many different levels. … A ...
Stochastic Processes with R: An Introduction
1st Edition
By Olga Korosteleva
May 27, 2024
Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. The light yet detailed text provides a solid foundation that is...
Theory of Statistical Inference
1st Edition
By Anthony Almudevar
May 27, 2024
Theory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or advanced undergraduate level. It presents a unified treatment of the foundational ideas of modern statistical inference, and would be suitable for a core course in a ...
Time Series for Data Science: Analysis and Forecasting
1st Edition
By Wayne A. Woodward, Bivin Philip Sadler, Stephen Robertson
May 27, 2024
Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. ...
Statistical Inference
2nd Edition
By George 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 natural extensions, and ...
Generalized Linear Mixed Models: Modern Concepts, Methods and Applications
2nd Edition
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 ...
Nonparametric Statistical Methods Using R
2nd Edition
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 ...
A Course in the Large Sample Theory of Statistical Inference
1st Edition
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...
Spatio–Temporal Methods in Environmental Epidemiology with R
2nd Edition
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 ...
Models for Multi-State Survival Data: Rates, Risks, and Pseudo-Values
1st Edition
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...
Time Series: Modeling, Computation, and Inference, Second Edition
2nd Edition
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 ...
Geographic Data Science with Python
1st Edition
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 ...