Statistics and Computing

, Volume 13, Issue 1, pp 67–80

An alternative to model selection in ordinary regression

  • Nicholas T. Longford
Article

DOI: 10.1023/A:1021995912647

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 testingmean squared errormodel selectionsingle-model based estimatorsynthetic estimatortwo-stage procedure

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Nicholas T. Longford
    • 1
  1. 1.De Montfort UniversityLeicesterUK