Non-linear structural equation models with correlated continuous and discrete data

被引：4
作者：
Lee, Sik-Yum [1 ]
Song, Xin-Yuan [1 ]
Cai, Jing-Heng [1 ]
So, Wing-Yee [2 ]
Ma, Ching-Wang [2 ]
Chan, Chung-Ngor Juliana [2 ]
机构：
[1] Chinese Univ Hong Kong, Dept Stat, Shatin, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Prince Wales Hosp, Dept Med & Therapeut, Shatin, Hong Kong, Peoples R China
来源：
BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY | 2009年 / 62卷
关键词：
LATENT VARIABLE MODELS; MAXIMUM-LIKELIHOOD-ESTIMATION; BAYESIAN-ANALYSIS; ALGORITHM;
D O I：
10.1348/000711008X292343
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
Structural equation models (SEMs) have been widely applied to examine interrelationships among latent and observed variables in social and psychological research. Motivated by the fact that correlated discrete variables are frequently encountered in practical applications, a non-linear SEM that accommodates covariates, and mixed continuous, ordered, and unordered categorical variables is proposed. Maximum likelihood methods for estimation and model comparison are discussed. One real-life data set about cardiovascular disease is used to illustrate the methodologies.
引用
收藏
页码：327 / 347
页数：21
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