CROSS-VALIDATION CRITERIA FOR COVARIANCE-STRUCTURES

被引：6

作者：

DEGOOIJER, JG ^{[1
]}

机构：

[1] UNIV AMSTERDAM,DEPT ECON STAT,1018 WB AMSTERDAM,NETHERLANDS

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 1995年 / 24卷 / 01期

关键词：

FACTOR ANALYSIS; MODEL-SELECTION CRITERIA;

D O I：

10.1080/03610919508813226

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A central issue in the analysis of covariance structures is the choice of a suitable model. In this paper two cross-validation (CV) criteria are presented for this purpose. For one of these criteria an asymptotically valid approximation is derived. This criterion can be used in conjunction with any correctly specified discrepancy function and is, in comparison with existing CV criteria, computationally less demanding. The performance of the proposed criterion is evaluated in a Monte Carlo study and compared to the results obtained from various other model-selection criteria, both in small- and large sample situations. An empirical example is given to illustrate its utility in practice. The results demonstrate the effectiveness of the proposed CV criterion for routinely assessing covariance structural models.

引用

页码：1 / 16

页数：16

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