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.

By **Alan Agresti, Maria Kateri**

November 30, 2021

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data ...

By **Sharon L. Lohr**

November 30, 2021

"The level is appropriate for an upper-level undergraduate or graduate-level statistics major. Sampling: Design and Analysis (SDA) will also benefit a non-statistics major with a desire to understand the concepts of sampling from a finite population. A student with patience to delve into the rigor ...

By **Darrin Speegle, Bryan Clair**

November 26, 2021

This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions ...

By **Babette A. Brumback**

November 10, 2021

"With this clear, rigorous, and readable presentation of causal inference concepts with basic principles of probabilities and statistics, Brumback’s text will greatly enhance the accessibility of causal inference to students, researchers and practitioners in a wide variety of disciplines."-Debashis...

By **Nalini Ravishanker, Zhiyi Chi, Dipak K. Dey**

October 19, 2021

Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is an intermediate-level statistics text that fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an ...

By **James Gentle**

September 30, 2021

Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of ...

By **Marco Scutari, Jean-Baptiste Denis**

July 29, 2021

Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the ...

By **Raquel Prado, Marco A. R. Ferreira, Mike West**

July 27, 2021

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 **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 ...

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 ...

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 ...

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 ...