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Scientific Inference, Data Analysis, and Robustness - 1st Edition - ISBN: 9780121211608, 9781483259390

Scientific Inference, Data Analysis, and Robustness

1st Edition

Proceedings of a Conference Conducted by the Mathematics Research Center, the University of Wisconsin—Madison, November 4–6, 1981

Editors: G. E. P. Box Tom Leonard Chien-Fu Wu
eBook ISBN: 9781483259390
Imprint: Academic Press
Published Date: 28th March 1983
Page Count: 316
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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.

Table of Contents




Pivotal Inference and the Conditional View of Robustness (Why have we for so Long Managed with Normality Assumptions?)

Some Philosophies of Inference and Modelling

Parametric Empirical Bayes Confidence Intervals

An Apology for Ecumenism in Statistics

Can Frequentist Inferences Be Very Wrong? A Conditional "Yes"

Frequency Properties of Bayes Rules

Purposes and Limitations of Data Analysis

Data Description

Likelihood, Shape, and Adaptive Inference

Statistical Inference and Measurement of Entropy

The Robustness of a Hierarchical Model for Multinomials and Contingency Tables

A Case Study of the Robustness of Bayesian Methods of Inference: Estimating the Total in a Finite Population Using Transformations to Normality

Estimation of Variance of the Ratio Estimator: An Empirical Study

Autocorrelation-Robust Design of Experiments



No. of pages:
© Academic Press 1983
28th March 1983
Academic Press
eBook ISBN:

About the Editors

G. E. P. Box

Tom Leonard

Chien-Fu Wu

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