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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Principles of Uncertainty
Statistical Machine Learning A Unified Framework
Probability and Bayesian Modeling
Practical Multivariate Analysis
Time Series A Data Analysis Approach Using R
John E. Kolassa
September 29, 2020
This textbook 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 include ...
Joseph B. Kadane
August 26, 2020
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 ...
Bryan F.J. Manly, Jorge A. Navarro Alberto
July 22, 2020
Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical ...
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, ...
March 16, 2020
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 statistics, the book pushes you to perform step-by-step calculations that are usually automated. ...
March 11, 2020
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 ...
Robert B. Gramacy
January 08, 2020
Surrogates: a graduate textbook, or professional handbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), design of experiments, and optimization. Experimentation through simulation, "human out-of-the-loop" statistical ...
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...
Tucker S. McElroy, Dimitris N. Politis
December 09, 2019
Time Series: A First Course with Bootstrap Starter provides an introductory course on time series analysis that satisfies the triptych of (i) mathematical completeness, (ii) computational illustration and implementation, and (iii) conciseness and accessibility to upper-level undergraduate and M.S. ...
Abdelmonem Afifi, Susanne May, Robin Donatello, Virginia A. Clark
October 14, 2019
This is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach, with excellent examples and good guidance on computing, the book is particularly popular for teaching outside statistics, i.e. in epidemiology, social science, business...
Robert Shumway, David 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 ...
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 ...