FITTING LONGITUDINAL REDUCED-RANK REGRESSION-MODELS BY ALTERNATING LEAST-SQUARES

被引：16

作者：

BIJLEVELD, CCJH ^{[1
]}

DELEEUW, J ^{[1
]}

机构：

[1] UNIV CALIF LOS ANGELES,DEPT PSYCHOL & MATH,LOS ANGELES,CA 90024

来源：

PSYCHOMETRIKA | 1991年 / 56卷 / 03期

关键词：

REDUCED-RANK REGRESSION; STATE SPACE ANALYSIS; OPTIMAL SCALING;

D O I：

10.1007/BF02294484

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

An alternating least squares method for iteratively fitting the longitudinal reduced-rank regression model is proposed. The method uses ordinary least squares and majorization substeps to estimate the unknown parameters in the system and measurement equations of the model. In an example with cross-sectional data, it is shown how the results conform closely to results from eigenanalysis. Optimal scaling of nominal and ordinal variables is added in a third substep, and illustrated with two examples involving cross-sectional and longitudinal data.

引用

页码：433 / 447

页数：15

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