On Model Uncertainty and its Statistical Implications

1. Front Matter

   Pages I-VII

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2. On the Impact of Variable Selection in Fitting Regression Equations

   • D. A. Freedman, W. Navidi, S. C. Peters

   Pages 1-16

3. Data-Driven Selection of Regressors and the Bootstrap

   • T. K. Dijkstra, J. H. Veldkamp

   Pages 17-38

4. Autocorrelation Pre-Testing in Linear Models with AR(1) Errors

   • Henk Folmer

   Pages 39-55

5. On Cross-Validation for Predictor Evaluation in Time Series

   • Tom A. B. Snijders

   Pages 56-69

6. Modification of Factor Analysis Models in Covariance Structure Analysis a Monte Carlo Study

   • Thom Luijben, Anne Boomsma, Ivo W. Molenaar

   Pages 70-101

7. Pitfalls for Forecasters

   • Ton Steerneman, Gertjan Rorijs

   Pages 102-117

8. Model Selection in Multinomial Experiments

   • Jan de Leeuw

   Pages 118-138

9. Back Matter

   Pages 139-141

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In this book problems related to the choice of models in such diverse fields as regression, covariance structure, time series analysis and multinomial experiments are discussed. The emphasis is on the statistical implications for model assessment when the assessment is done with the same data that generated the model. This is a problem of long standing, notorious for its difficulty. Some contributors discuss this problem in an illuminating way. Others, and this is a truly novel feature, investigate systematically whether sample re-use methods like the bootstrap can be used to assess the quality of estimators or predictors in a reliable way given the initial model uncertainty. The book should prove to be valuable for advanced practitioners and statistical methodologists alike.

• Estimator
• Factor analysis
• Fitting
• Time series
• Variance
• best fit
• calculus
• correlation
• statistical method

• Institute of Econometrics, University of Groningen, Groningen, The Netherlands

  Theo K. Dijkstra

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