Non-linear structural equation models with correlated continuous and discrete data.
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.
Lee
Sik-Yum
SY
Department of Statistics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. sylee@sta.cuhk.edu.hk
Song
Xin-Yuan
XY
Cai
Jing-Heng
JH
So
Wing-Yee
WY
Ma
Ching-Wang
CW
Chan
Chung-Ngor Juliana
CN
D000465:Algorithms; D000483:Alleles; D000704:Analysis of Variance; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000235:genetics; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology / Q000235:genetics; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005838:Genotype; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011584:Psychology / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012307:Risk Factors
D005819:Genetic Markers