Ebook: On Model Uncertainty and its Statistical Implications: Proceedings of a Workshop, Held in Groningen, The Netherlands, September 25–26, 1986

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.

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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.

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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.
Content:
Front Matter....Pages I-VII
On the Impact of Variable Selection in Fitting Regression Equations....Pages 1-16
Data-Driven Selection of Regressors and the Bootstrap....Pages 17-38
Autocorrelation Pre-Testing in Linear Models with AR(1) Errors....Pages 39-55
On Cross-Validation for Predictor Evaluation in Time Series....Pages 56-69
Modification of Factor Analysis Models in Covariance Structure Analysis a Monte Carlo Study....Pages 70-101
Pitfalls for Forecasters....Pages 102-117
Model Selection in Multinomial Experiments....Pages 118-138
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.
Content:
Front Matter....Pages I-VII
On the Impact of Variable Selection in Fitting Regression Equations....Pages 1-16
Data-Driven Selection of Regressors and the Bootstrap....Pages 17-38
Autocorrelation Pre-Testing in Linear Models with AR(1) Errors....Pages 39-55
On Cross-Validation for Predictor Evaluation in Time Series....Pages 56-69
Modification of Factor Analysis Models in Covariance Structure Analysis a Monte Carlo Study....Pages 70-101
Pitfalls for Forecasters....Pages 102-117
Model Selection in Multinomial Experiments....Pages 118-138
Back Matter....Pages 139-141
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