Modelling and Prediction Honoring Seymour Geisser

1. Front Matter

   Pages i-xvi

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2. General Methodological Issues

   1. Front Matter

      Pages 1-1

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   2. Some Statistical Issues in Forensic DNA Profiling

      • Seymour Geisser

      Pages 3-18

   3. Statistical Practice as Argumentation: A Sketch of a Theory of Applied Statistics

      • James S. Hodges

      Pages 19-45

   4. Individual Rationality and Social Efficiency in an Information Contagion Model

      • David Lane

      Pages 46-60

   5. Bayesian Method of Moments (BMOM) Analysis of Mean and Regression Models

      • Arnold Zellner

      Pages 61-72

3. Prediction

   1. Front Matter

      Pages 75-75

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   2. On the Prediction of Growth Curves

      • Jack C. Lee, Seymour Geisser

      Pages 77-103

   3. Predictive Influence in the Log Normal Survival Model

      • Wesley O. Johnson

      Pages 104-121

   4. Selecting the Form of Combining Regressions Based on Recur sive Prediction Criteria

      • Liang Kou-yuan, Ryu Keunkwan

      Pages 122-135

   5. Multiperiod-Ahead Predictive Densities and Model Comparison in Dynamic Models

      • Min Chung-ki

      Pages 136-148

   6. Bayesian Prediction Under Asymmetric Linear Loss: Forecasting State Tax Revenues in Iowa

      • Charles H. Whiteman

      Pages 149-166

4. Design of Experiments and Classification

   1. Front Matter

      Pages 167-167

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   2. On small sample Bayesian inference and sequential design for quantal response curves

      • Tom Leonard, John S. J. Hsu

      Pages 169-175

   3. Some Simulation Results for the Performance of DNA Classification of Noisy Image Data

      • S. James Press

      Pages 176-192

   4. Case-Control Studies and Bayesian Inference

      • M. Zelen, R. A. Parker

      Pages 193-203

5. Prior Distributions and Estimation

   1. Front Matter

      Pages 205-205

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   2. Orthogonalizations and Prior Distributions for Orthogonalized Model Mixing

      • Merlise Clyde, Giovanni Parmigiani

      Pages 206-227

   3. Improved Quasi-Maximum Likelihood Estimation for Stochastic Volatility Models

      • F. Jay Breidt, Alicia L. Carriquiry

      Pages 228-247

   4. Bayesian Inference for Linear Models Subject to Linear Inequality Constraints

      • John F. Geweke

      Pages 248-263

Modelling and Prediction Honoring Seymour Geisser contains the refereed proceedings of the Conference on Forecasting, Prediction, and Modelling held at National Chiao Tung University, Taiwan in 1994. The papers discuss general methodological issues; prediction; design of experiments and classification; prior distributions and estimation; posterior odds, testing, and model selection; modelling and prediction in finance; and time series modelling and applications. Specific topics include very interesting and topical statistical issues related to DNA fingerprinting and spatial image reconstruction, foundational issues for applied statistics and testing hypotheses, forecasting tax revenues and bond prices, and assessing oxone depletion.

• DNA
• Statistica
• applied statistics
• classification
• design
• distribution
• fingerprint
• model
• modeling
• modelling
• selection
• statistics
• testing
• time
• time series

• Institute of Statistics, National Chiao Tung University, Hsichu, Taiwan R.O.C

  Jack C. Lee

• Department of Statistics, University of California, Davis, Davis, USA

  Wesley O. Johnson

• Graduate School of Business, University of Chicago, Chicago, USA

  Arnold Zellner

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