Bayesian Analysis in Statistics and Econometrics

1. Front Matter

   Pages i-ix

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2. Computational Approaches

   1. LaPlace Approximation for Curved Surfaces

      • Joseph B. Kadane, Parthasarathy Bagchi

      Pages 1-12

   2. Designing a Bayesian Object-Oriented Computing Environment

      • William DuMouchel

      Pages 13-26

3. Contingency Tables and Nonparametric Bayes

   1. Bayesian Estimation in Multidimensional Contingency Tables

      • Leonardo D. Epstein, Stephen E. Fienberg

      Pages 27-41

   2. Bayesian Nonparametric Prediction and Statistical Inference

      • Bruce M. Hill

      Pages 43-94

   3. Homogeneity of Subpopulations for Two-By-Two Contingency Tables

      • Yashaswini Mittal

      Pages 95-106

4. Hierarchical & Empirical Bayes Inference

   1. A Hierarchical Bayes Approach to Small Area Estimation with Auxiliary Information

      • Malay Ghosh, Parthasarathi Lahiri

      Pages 107-125

   2. On Empirical Bayes Selection Rules for Negative Binomial Populations

      • Shanti S. Gupta, TaChen Liang

      Pages 127-146

   3. Empirical Hierarchical Bayes Estimation

      • C. G. Khatri, C. Radhakrishna Rao

      Pages 147-161

   4. Simulation Comparison of Methods for Bayesian Contextual Classification of Remotely Sensed Data

      • Ruben Klein, S. James Press

      Pages 163-175

5. Priors & Utilities

   1. Reference Priors in a Variance Components Problem

      • James O. Berger, José M. Bernardo

      Pages 177-194

   2. An Elicitation Procedure Using Piecewise Conjugate Priors

      • Glen Meeden

      Pages 195-206

   3. Small Worlds and State Dependent Utilities

      • Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kadane

      Pages 207-215

6. Reflections on Bayesian Paradigm

   1. Learning Statistics from Counter Examples: Ancillary Statistics

      • D. Basu

      Pages 217-223

   2. The Horvitz-Thompson Estimate and Basu’s Circus Revisited

      • J. K. Ghosh

      Pages 225-228

   3. Comparison of Experiments for Selection and Censored Data Models

      • Prem K. Goel, Morris H. DeGroot

      Pages 229-247

   4. Jeffreys-Lindley Paradox and a Related Problem

      • Sudhakar Kunte

      Pages 249-255

7. Reliability & Dose Response Modeling

   1. Bayesian Approach to Some Problems in Life Testing and Reliability Estimation

      • Asit P. Basu, Nader Ebrahimi

      Pages 257-266

   2. When to Stop Testing Software? Some Exact Results

      • S. R. Dalai, C. L. Mallows

      Pages 267-276

   3. Filtering, Smoothing, and Extrapolations in Dose-Response Experiments: Application to Data on Respiratory Tumors in Rats

      • Nozer D. Singpurwalla, Jingxian Chen

      Pages 277-288

This volume is based on the invited and the contributed presentations given at the Indo-U.S. Workshop on Bayesian Analysis in Statistics and Econometrics (BASE), Dec. 19-23, 1988, held at the Hotel Taj Residency, Bangalore, India. The workshop was jointly sponsored by The Ohio State University, The Indian Statistical Institute, The Indian Econometrics So­ ciety, U.S. National Science Foundation and the NSF-NBER Seminar on Bayesian Inference in Econometrics. Profs. Morrie DeGroot, Prem Goel, and Arnold Zellner were the program organizers. Unfortunately, Morrie became seriously ill just before the workshop was to start and could not participate in the workshop. Almost a year later, Morrie passed away after fighting valiantly with the illness. Not to find Morrie among ourselves was a shock for most of us. He was a continuous source of inspiration and ideas. Even while Morrie was fighting for his life, we had a lot of discussions about the contents of this volume and the Bangalore Workshop. He even talked about organizing a Second Indo-U.S. workshop some time in the near future. We are dedicating this volume to the memory of Prof. Morris H. DeGroot. We have taken a conscious decision not to include any biography of Morrie in this volume. An excellent biography of Morrie has appeared in Statistical Science [(1991), vol. 6, 1-14], and we could not have done a better job than that.

• Simulation
• Truncated Data
• Variance
• econometrics
• forecasting
• linear regression
• modeling
• poverty
• regression
• statistical inference
• statistics
• time series

• Department of Statistics, The Ohio State University, Columbus, USA

  Prem K. Goel

• Economic Analysis Unit, Indian Statistical Institute, Bangalore, Bangalore, India

  N. Sreenivas Iyengar

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