Scientific Inference, Data Analysis, and Robustness

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

G. E. P. Box, Tom Leonard, Chien-Fu Wu

• Publisher: Academic Press
• Year: 2014
• Print ISBN:
  9780121211608
• eBook ISBN:
  9781483259390
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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

• Front Cover
• Scientific Inference, Data Analysis, and Robustness
• Copyright Page
• Table of Contents
• Contributors
• Foreword
• Preface
• Chapter 1. Pivotal Inference and the Conditional View of Robustness (Why have we for so long managed with normality assumptions?)
• Chapter 2. Some Philosophies of Inference and Modelling
• Chapter 3. Parametric Empirical Bayes Confidence Intervals
• Chapter 4. An Apology for Ecumenism in Statistics
• Chapter 5. Can Frequentist Inferences Be Very Wrong? A Conditional "Yes"
• Chapter 6. Frequency Properties of Bayes Rules
• Chapter 7. Purposes and Limitations of Data Analysis
• Chapter 8. Data Description
• Chapter 9. Likelihood, Shape, and Adaptive Inference
• Chapter 10. Statistical Inference and Measurement of Entropy
• Chapter 11. The Robustness of a Hierarchical Model for Multinomials and Contingency Tables
• Chapter 12. A Case Study of the Robustness of Bayesian Methods of Inference: Estimating the Total in a Finite Population Using Transformations to Normality
• Chapter 13. Estimation of Variance of the Ratio Estimator: An Empirical Study
• Chapter 14. Autocorrelation-robust Design of Experiments
• Index