An alternative to model selection in ordinary regression
Cite this article as: Longford, N.T. Statistics and Computing (2003) 13: 67. doi:10.1023/A:1021995912647 Abstract
The weaknesses of established model selection procedures based on hypothesis testing and similar criteria are discussed and an alternative based on synthetic (composite) estimation is proposed. It is developed for the problem of prediction in ordinary regression and its properties are explored by simulations for the simple regression. Extensions to a general setting are described and an example with multiple regression is analysed. Arguments are presented against using a selected model for any inferences.
hypothesis testing mean squared error model selection single-model based estimator synthetic estimator two-stage procedure References
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