Scientific inference, data analysis, and robustness : proceedings of a conference conducted by the Mathematics Research Center,Univ.Wisconsin,1981 / edited by Box, Leonard, Fu

Box, George E. P. (George Edward Pelham). Leonard, Tom. Wu, Chien-Fu

ISBN 10: 0121211606 / ISBN 13: 9780121211608

Published by New York : Academic Press, 1983

About this Item

Near fine cloth copy in a very good if slightly edge-nicked and dust-dulled dust-wrapper, now mylar-sleeved. Minor library marks remain (internally). Remains particularly well-preserved overall; tight, bright, clean and strong. Physical description; 304 pp. Subjects; Statistical Methods. Congresses. Robust statistics. Mathematics. 1 Kg.

Seller Inventory # 397224

Bibliographic Details

• Title: Scientific inference, data analysis, and robustness : proceedings of a conference conducted by the Mathematics Research Center,Univ.Wisconsin,1981 / edited by Box, Leonard, Fu
• Publisher: New York : Academic Press
• Publication Date: 1983
• Language: English
• ISBN 10: 0121211606
• ISBN 13: 9780121211608
• Binding: Hardcover
• Edition: 1st edition.
• Weight: 1 kilogram

About this title

Synopsis

Mathematics Research Center Symposium: Scientific Inference, Data Analysis, and Robustness focuses on the philosophy of statistical modeling, including model robust inference and analysis of data sets. The selection first elaborates on pivotal inference and the conditional view of robustness and some philosophies of inference and modeling, including ideas on modeling, significance testing, and scientific discovery. The book then ponders on parametric empirical Bayes confidence intervals, ecumenism in statistics, and frequency properties of Bayes rules. Discussions focus on consistency of Bayes rules, scientific method and the human brain, and statistical estimation and criticism. The book takes a look at the purposes and limitations of data analysis, likelihood, shape, and adaptive inference, statistical inference and measurement of entropy, and the robustness of a hierarchical model for multinomials and contingency tables. Topics include numerical results for contingency tables and robustness, multinomials, flattening constants, and mixed Dirichlet priors, entropy and likelihood, and test as measurement of entropy. The selection is a valuable reference for researchers interested in robust inference and analysis of data sets.