On Model Uncertainty and its Statistical Implications

Theo K. Dijkstra

ISBN 10: 3540193677 / ISBN 13: 9783540193678

Published by Springer, Springer Vieweg Mai 1988, 1988

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 152 pp. Englisch.

Seller Inventory # 9783540193678

Bibliographic Details

• Title: On Model Uncertainty and its Statistical Implications
• Publisher: Springer, Springer Vieweg Mai 1988
• Publication Date: 1988
• Language: English
• ISBN 10: 3540193677
• ISBN 13: 9783540193678
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 9D millimeters 254 millimeters width by 178 millimeters height by 9 millimeters depth
• Weight: 300 grams

About this title

Synopsis

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.