Observed Confidence Levels : Theory and Application book cover
1st Edition

Observed Confidence Levels
Theory and Application

ISBN 9780367388423
Published September 19, 2019 by Chapman and Hall/CRC
288 Pages

USD $74.95

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Book Description

Illustrating a simple, novel method for solving an array of statistical problems, Observed Confidence Levels: Theory and Application describes the basic development of observed confidence levels, a methodology that can be applied to a variety of common multiple testing problems in statistical inference. It focuses on the modern nonparametric framework of bootstrap-based estimates, allowing for substantial theoretical development and for relatively simple solutions to numerous interesting problems.

After an introduction, the book develops the theory and application of observed confidence levels for general scalar parameters, vector parameters, and linear models. It then examines nonparametric problems often associated with smoothing methods, including nonparametric density estimation and regression. The author also describes applications in generalized linear models, classical nonparametric statistics, multivariate analysis, and survival analysis as well as compares the method of observed confidence levels to hypothesis testing, multiple comparisons, and Bayesian posterior probabilities. In addition, the appendix presents some background material on the asymptotic expansion theory used in the book.

Helping you choose the most reliable method for a variety of problems, this book shows how observed confidence levels provide useful information on the relative truth of hypotheses in multiple testing problems.

Table of Contents

Preface. Introduction. Single Parameter Problems. Multiple Parameter Problems. Linear Models and Regression. Nonparametric Smoothing Problems. Further Applications. Connections and Comparisons. Appendix. References. Index.

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Polansky, Alan M.