Bayesian Analysis in Statistics and Econometrics

Goel, Prem K.|Iyengar, N. Sreenivas

ISBN 10: 0387978631 / ISBN 13: 9780387978635

Editore: Springer New York, 1992

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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 s. Codice articolo 5913122

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Titolo: Bayesian Analysis in Statistics and ...

Casa editrice: Springer New York

Data di pubblicazione: 1992

Legatura: Kartoniert / Broschiert

Condizione: New

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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.

Contenuti:

A Computational Approaches.- 1. Laplace Approximation for Curved Surfaces.- 2. Designing a Bayesian Object-Oriented Computing Environment.- B Contingency Tables and Nonparametric Bayes.- 3. Bayesian Estimation in Multidimensional Contingency Tables.- 4. Bayesian Nonparametric Prediction and Statistical Inference.- 5. Homogeneity of Subpopulations for Two-by Two Contingency Tables.- C Hierarchical & Empirical Bayes Inference.- 6. A Hierarchical Bayes Approach to Small Area Estimation with Auxiliary Information.- 7. On Empirical Bayes Selection Rules for Negative Binomial Populations.- 8. Empirical Hierarchical Bayes Estimation.- 9. Simulation Comparison of Methods for Bayesian Contextual Classification of Remotely Sensed Data.- D Priors & Utilities.- 10. Reference Priors in a Variance Components Problem.- 11. An Elicitation Procedure Using Piecewise Conjugate Priors.- 12. Small Worlds and State Dependent Utilities.- E Reflections on Bayesian Paradigm.- 13. Learning Statistics from Counter Examples: Ancillary Statistics.- 14. The Horvitz-Thompson Estimate and Basu’s Circus Revisited.- 15. Comparison of Experiments for Selection and Censored Data Models.- 16. Jeffreys-Lindley Paradox and a Related Problem.- F Reliability & Dose Response Modeling.- 17. Bayesian Approach to Some Problems in Life Testing and Reliability Estimation.- 18. When to Stop Testing Software? Some Exact Results.- 19. Filtering, Smoothing, and Extrapolations in Dose-Response Experiments: Application to Data on Respiratory Tumors in Rats.- G Time Series Modeling, Forecasting and Robustness.- 20. Bayesian Perturbation Diagnostics and Robustness.- 21. Forecasting Similar Time Series with Bayesian Pooling Methods: Applications to Forecasting European Output Growth.- 22. Forecasting International Growth Rates Using Bayesian Shrinkage and Other Procedures.- H Contributed Papers.- 23. Estimation in the Linear Regression Model with Errors in Variables.- 24. Population Size Estimation with Truncated Data: A Survey.- 25. Bayesian Analysis of Co-Integrated Time Series.- 26. A Bayesian Approach to the Measurement of Poverty in India.- 27. Quantifying Prior Opinion in Length Biased Linear Mean Natural Exponential Family.- Author Index.- List of Participants.