On Model Uncertainty and its Statistical Implications [electronic resource] : Proceedings of a Workshop, Held in Groningen, The Netherlands, September 25-26, 1986 / edited by Theo K. Dijkstra
Imprint
Berlin, Heidelberg : Springer Berlin Heidelberg, 1988
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
On the impact of variable selection in fitting regression equations. -- Data-driven selection of regressors and the bootstrap. -- Autocorrelation pre-testing in linear models with AR(1) errors. -- On cross-validation for predictor evaluation in time series. -- Modification of factor analysis models in covariance structure analysis. A Monte Carlo study. -- Pitfalls for forecasters. -- Model selection in multinomial experiments
