Analysis of conditional covariance structure models
type: Item
instance of: scholarly article
title: Analysis of conditional covariance structure models
zbMATH Open document ID: 0588.62094
DOI: 10.1016/0167-9473(86)90025-3
author: Sik Lee
published in: Computational Statistics and Data Analysis
publication date: 1986-01-01T00:00:00Z
review text: This paper presents a statistical development for analyzing covariance structure models with certain variables held constant. The theory enables one to assess partial correlations among variables. Models are defined based on the conditional covariance matrices, and they are studied by the maximum likelihood approach with the appropriate conditional sample covariance matrices. It is shown that existing computer package programs and statistical methods can be easily applied to analyze the new models. Examples from two real data sets are reported to illustrate the theory.
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 62H20
zbMATH DE Number: 3945120
zbMATH Keywords: partial factor analysis
zbMATH Keywords: part and bipartial factor analysis
zbMATH Keywords: partial LISREL model
zbMATH Keywords: goodness-of-fit test
zbMATH Keywords: covariance structure models
zbMATH Keywords: partial correlations
zbMATH Keywords: conditional covariance matrices
zbMATH Keywords: maximum likelihood approach
MaRDI profile type: Publication
full work available at URL: 0167-9473(86)90025-3
OpenAlex ID: W2069481752
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description: scientific article; zbMATH DE number 3945120