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.
Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects
1st Edition
By James S. Hodges
June 30, 2021
A First Step toward a Unified Theory of Richly Parameterized Linear Models Using mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based ...
Modern Data Science with R
2nd Edition
By Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
April 14, 2021
From a review of the first edition: "Modern Data Science with R… is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American ...
Bayesian Thinking in Biostatistics
1st Edition
By Gary L Rosner, Purushottam W. Laud, Wesley O. Johnson
March 16, 2021
Praise for Bayesian Thinking in Biostatistics: "This thoroughly modern Bayesian book …is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array of ...
Linear Models with Python
1st Edition
By Julian J. Faraway
December 28, 2020
Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. … It lays down the material in ...
An Introduction to Nonparametric Statistics
1st Edition
By John E. Kolassa
September 29, 2020
An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well. These techniques ...
Statistical Machine Learning: A Unified Framework
1st Edition
By Richard Golden
July 02, 2020
The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, ...
Statistical Rethinking: A Bayesian Course with Examples in R and STAN
2nd Edition
By Richard McElreath
March 16, 2020
Winner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA) Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based ...
Probability and Bayesian Modeling
1st Edition
By Jim Albert, Jingchen Hu
December 18, 2019
Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and...
Time Series: A Data Analysis Approach Using R
1st Edition
By Robert H. Shumway, David S. Stoffer
May 21, 2019
The goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. A useful feature of the presentation is the inclusion of nontrivial data sets illustrating the richness of potential ...
The Analysis of Time Series: An Introduction with R
7th Edition
By Chris Chatfield, Haipeng Xing
May 09, 2019
This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear ...
Theory of Spatial Statistics: A Concise Introduction
1st Edition
By M.N.M. van Lieshout
March 11, 2019
Theory of Spatial Statistics: A Concise Introduction presents the most important models used in spatial statistics, including random fields and point processes, from a rigorous mathematical point of view and shows how to carry out statistical inference. It contains full proofs, ...
Introduction to Probability, Second Edition
2nd Edition
By Joseph K. Blitzstein, Jessica Hwang
February 08, 2019
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google ...