Hostname: page-component-6766d58669-bp2c4 Total loading time: 0 Render date: 2026-05-17T05:33:36.138Z Has data issue: false hasContentIssue false

REGRESSION DEPENDENCE IN LATENT VARIABLE MODELS

Published online by Cambridge University Press:  06 March 2006

Taizhong Hu
Affiliation:
Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China, E-mail: thu@ustc.edu.cn
Jing Chen
Affiliation:
Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China, E-mail: thu@ustc.edu.cn
Chaode Xie
Affiliation:
Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China, E-mail: thu@ustc.edu.cn

Abstract

Three new notions of positive dependence (positive regression dependence, positive left-tail regression dependence, and positive right-tail regression dependence) are studied in this article. Consider a latent variable model where the manifest random variables T1,T2,…,Tn given latent random variable/vector (Θ1,…,Θm) are conditional independent. Conditions are identified under which T1,…,Tn possesses the new dependence notions for different types of latent variable model. Applications of the results are also provided.

Information

Type
Research Article
Copyright
© 2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable