The Collected Works of John W. Tukey

The Collected Works of John W. Tukey
Author: Jeff Austin. Brillinger
Publisher: CRC Press
Total Pages: 764
Release: 1984-02-01
Genre: Mathematics
ISBN: 9780412742408

First of an eight-volume set, documenting Tukey's work from the 1940s to the 1980s One of the late 20th Century's leading innovators and influences on data analysis, John W. Tukey's discoveries and methods have greatly impacted the work of statisticians throughout the world. The Collected Works of John W. Tukey begins here, with 14 chapters on time series analysis.

The Practice of Data Analysis

The Practice of Data Analysis
Author: David R. Brillinger
Publisher: Princeton University Press
Total Pages: 352
Release: 2014-07-14
Genre: Mathematics
ISBN: 1400851602

This collection of essays brings together many of the world's most distinguished statisticians to discuss a wide array of the most important recent developments in data analysis. The book honors John W. Tukey, one of the most influential statisticians of the twentieth century, on the occasion of his eightieth birthday. Contributors, some of them Tukey's former students, use his general theoretical work and his specific contributions to Exploratory Data Analysis as the point of departure for their papers. They cover topics from "pure" data analysis, such as gaussianizing transformations and regression estimates, and from "applied" subjects, such as the best way to rank the abilities of chess players or to estimate the abundance of birds in a particular area. Tukey may be best known for coining the common computer term "bit," for binary digit, but his broader work has revolutionized the way statisticians think about and analyze sets of data. In a personal interview that opens the book, he reviews these extraordinary contributions and his life with characteristic modesty, humor, and intelligence. The book will be valuable both to researchers and students interested in current theoretical and practical data analysis and as a testament to Tukey's lasting influence. The essays are by Dhammika Amaratunga, David Andrews, David Brillinger, Christopher Field, Leo Goodman, Frank Hampel, John Hartigan, Peter Huber, Mia Hubert, Clifford Hurvich, Karen Kafadar, Colin Mallows, Stephan Morgenthaler, Frederick Mosteller, Ha Nguyen, Elvezio Ronchetti, Peter Rousseeuw, Allan Seheult, Paul Velleman, Maria-Pia Victoria-Feser, and Alessandro Villa. Originally published in 1998. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

Handbook of Psychology, Research Methods in Psychology

Handbook of Psychology, Research Methods in Psychology
Author: John A. Schinka
Publisher: John Wiley & Sons
Total Pages: 737
Release: 2003-03-19
Genre: Psychology
ISBN: 0471264431

Includes established theories and cutting-edge developments. Presents the work of an international group of experts. Presents the nature, origin, implications, an future course of major unresolved issues in the area.

Handbook of Psychology, Research Methods in Psychology

Handbook of Psychology, Research Methods in Psychology
Author: Irving B. Weiner
Publisher: John Wiley & Sons
Total Pages: 744
Release: 2003-01-03
Genre: Medical
ISBN: 9780471385134

Includes established theories and cutting-edge developments. Presents the work of an international group of experts. Presents the nature, origin, implications, an future course of major unresolved issues in the area.

Testing Statistical Hypotheses

Testing Statistical Hypotheses
Author: E.L. Lehmann
Publisher: Springer Nature
Total Pages: 1016
Release: 2022-06-22
Genre: Mathematics
ISBN: 3030705781

The third edition of Testing Statistical Hypotheses updates and expands upon the classic graduate text, emphasizing optimality theory for hypothesis testing and confidence sets. The principal additions include a rigorous treatment of large sample optimality, together with the requisite tools. In addition, an introduction to the theory of resampling methods such as the bootstrap is developed. The sections on multiple testing and goodness of fit testing are expanded. The text is suitable for Ph.D. students in statistics and includes over 300 new problems out of a total of more than 760.

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics
Author: John E. Kolassa
Publisher: CRC Press
Total Pages: 225
Release: 2020-09-28
Genre: Mathematics
ISBN: 0429511361

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 include one-sample testing and estimation, multi-sample testing and estimation, and regression. Attention is paid to the intellectual development of the field, with a thorough review of bibliographical references. Computational tools, in R and SAS, are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign, Kruskal-Wallis, Friedman, Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology, elementary probability, and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration, and, ideally, a course in matrix algebra.

Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R

Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R
Author: Dan Lin
Publisher: Springer Science & Business Media
Total Pages: 285
Release: 2012-08-27
Genre: Mathematics
ISBN: 3642240070

This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students. Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as inference under order restrictions and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include: • Multiplicity adjustment • Test statistics and procedures for the analysis of dose-response microarray data • Resampling-based inference and use of the SAM method for small-variance genes in the data • Identification and classification of dose-response curve shapes • Clustering of order-restricted (but not necessarily monotone) dose-response profiles • Gene set analysis to facilitate the interpretation of microarray results • Hierarchical Bayesian models and Bayesian variable selection • Non-linear models for dose-response microarray data • Multiple contrast tests • Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rate All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.