The mixture index of fit and minimax regression
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- 作者：Tam谩s Rudas
- 关键词：Key words ; Minimax estimation ; minimum distance estimation ; mixture index of fit ; regression with uniform error ; variable selection
- 刊名：Metrika
- 出版时间：January 2000
- 出版年：2000
- 期刊代码：111_0026-1335
- 类别：mat
- 卷：50
- 期：2
- 页码：163-172
- 数据来源：sp
摘要
A measure of the fit of a statistical model can be obtained by estimating the relative size of the largest fraction of the population where a distribution belonging to the model may be valid. This is the mixture index of fit that was suggested for models for contingency tables by Rudas, Clogg, Lindsay (1994) and it is extended here for models involving continuous observations. In particular, the approach is applied to regression models with normal and uniform error structures. Best fit, as measured by the mixture index of fit, is obtained with minimax estimation of the regression parameters. Therefore, whenever minimax estimation is used for these problems, the mixture index of fit provides a natural approach for measuring model fit and for variable selection.