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 Peter .J. Bickel, Kjell A. Doksum
December 01, 2015
This package includes both Mathematical Statistics: Basic Ideas and Selected Topics, Volume I, Second Edition, as well as Mathematical Statistics: Basic Ideas and Selected Topics, Volume II. Volume I presents fundamental, classical statistical concepts at the doctorate level without using measure ...
By Peter J. Bickel, Kjell A. Doksum
November 02, 2015
Mathematical Statistics: Basic Ideas and Selected Topics, Volume II presents important statistical concepts, methods, and tools not covered in the authors’ previous volume. This second volume focuses on inference in non- and semiparametric models. It not only reexamines the procedures introduced in...
By Gavin Shaddick, James V. Zidek
June 24, 2015
Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological StudiesSpatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the ...
By Peter J. Bickel, Kjell A. Doksum
April 13, 2015
Mathematical Statistics: Basic Ideas and Selected Topics, Volume I, Second Edition presents fundamental, classical statistical concepts at the doctorate level. It covers estimation, prediction, testing, confidence sets, Bayesian analysis, and the general approach of decision theory. This edition ...
By John Lawson
December 17, 2014
Design and Analysis of Experiments with R presents a unified treatment of experimental designs and design concepts commonly used in practice. It connects the objectives of research to the type of experimental design required, describes the process of creating the design and collecting the data, ...
By John Kloke, Joseph McKean
October 09, 2014
A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear ...
By Helio S. Migon, Dani Gamerman, Francisco Louzada
September 03, 2014
A Balanced Treatment of Bayesian and Frequentist Inference Statistical Inference: An Integrated Approach, Second Edition presents an account of the Bayesian and frequentist approaches to statistical inference. Now with an additional author, this second edition places a more balanced emphasis on ...
By Christopher R. Bilder, Thomas M. Loughin
August 11, 2014
Learn How to Properly Analyze Categorical DataAnalysis of Categorical Data with R presents a modern account of categorical data analysis using the popular R software. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses ...
By Julian J. Faraway
July 01, 2014
A Hands-On Way to Learning Data Analysis Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with...
By Sadanori Konishi
June 06, 2014
Select the Optimal Model for Interpreting Multivariate Data Introduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random ...
By Sudipto Banerjee, Anindya Roy
June 06, 2014
Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge ...
By Francisco J. Samaniego
January 14, 2014
Provides a Solid Foundation for Statistical Modeling and Inference and Demonstrates Its Breadth of Applicability Stochastic Modeling and Mathematical Statistics: A Text for Statisticians and Quantitative Scientists addresses core issues in post-calculus probability and statistics in a way that is ...