REGRESSION COMPONENT DECOMPOSITION IN STRUCTURAL-ANALYSIS

被引：5

作者：

HAAGEN, K

VITTADINI, G

机构：

[1] UNIV TRENTO,IST STAT,I-38100 TRENT,ITALY

[2] UNIV BRESCIA,IST STAT,I-25122 BRESCIA,ITALY

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 1991年 / 20卷 / 04期

关键词：

STRUCTURAL MODEL; FACTOR ANALYSIS; LISREL MODEL; IDENTIFICATION; REGRESSION COMPONENT DECOMPOSITION;

D O I：

10.1080/03610929108830556

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 crucial problem in the Lisrel model remains that of finding necessary and sufficient conditions for the identification of the parameters. But even if the parameters in a particular model are identifiable, there remains an indeterminacy of the scores of the latent variables. To avoid this problem an alternative approach to the Lisrel model is here proposed, one that is based on a decomposition of the datamatrix in such a way that the assumptions in the Lisrel model are satisfied.

引用

页码：1153 / 1161

页数：9

共 11 条

[1]

Elffers H., 1978, STAT NEERL, V32, P181

[2]

HAAGEN K, 1983, QUADERNI STATISTICA, V6, P21

[3]

HAAGEN K, 1990, UNPUB NECESSARY SUFF

[4]

HAAGEN K, 1986, OSTERREICHISCHE Z ST, V15, P197