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.
Introduction to Design and Analysis of Experiments and Observational Studies using R
Theory of Statistical Inference
Sampling Design and Analysis
Fundamentals of Causal Inference With R
A First Course in Linear Model Theory
By Nicholas P. Jewell, Alan Hubbard
April 18, 2022
Aimed at a nontechnical audience, with intuitive explanations instead of mathematical derivations, Analysis of Longitudinal Studies in Epidemiology covers a wide range of topics that include Poisson regression, survival analysis, repeated measure, clustered data, longitudinal observations, and ...
By Wayne A. Woodward, Bivin Philip Sadler, Stephen Robertson
April 13, 2022
Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Practical Time Series Analysis for Data Science discusses techniques of ensemble modelling for combining information from several strategies. Covering time...
By Nathan Taback
March 22, 2022
Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design, data ...
By Olga Korosteleva
February 17, 2022
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...
By Alicia A. Johnson, Miles Q. Ott, Mine Dogucu
January 25, 2022
An engaging, sophisticated, and fun introduction to the field of Bayesian Statistics, Bayes Rules! An Introduction to Bayesian Modeling with R brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, it is an ideal resource for advanced undergraduate...
By Osvaldo A. Martin, Ravin Kumar, Junpeng Lao
December 29, 2021
Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying ...
By Anthony Almudevar
December 22, 2021
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 ...
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
One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference explains and relates different methods of confounding adjustment in terms of ...
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 ...