A Bayesian modeling approach for generalized semiparametric structural equation models

Psychometrika
Xin-Yuan SongEdward Hak-Sing Ip

Abstract

In behavioral, biomedical, and psychological studies, structural equation models (SEMs) have been widely used for assessing relationships between latent variables. Regression-type structural models based on parametric functions are often used for such purposes. In many applications, however, parametric SEMs are not adequate to capture subtle patterns in the functions over the entire range of the predictor variable. A different but equally important limitation of traditional parametric SEMs is that they are not designed to handle mixed data types-continuous, count, ordered, and unordered categorical. This paper develops a generalized semiparametric SEM that is able to handle mixed data types and to simultaneously model different functional relationships among latent variables. A structural equation of the proposed SEM is formulated using a series of unspecified smooth functions. The Bayesian P-splines approach and Markov chain Monte Carlo methods are developed to estimate the smooth functions and the unknown parameters. Moreover, we examine the relative benefits of semiparametric modeling over parametric modeling using a Bayesian model-comparison statistic, called the complete deviance information criterion (DIC). The performanc...Continue Reading

References

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Jun 10, 2010·Biometrical Journal. Biometrische Zeitschrift·Xin-Yuan SongPing-Chung Leung
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Citations

Dec 18, 2013·Statistics in Medicine·Xinyuan SongXiangnan Feng
Nov 27, 2015·Multivariate Behavioral Research·Holger Brandt, Andreas G Klein
Jan 31, 2019·Biostatistics·Klaus Kähler Holst, Esben Budtz-Jørgensen
Jan 13, 2018·Multivariate Behavioral Research·Jingheng CaiXinyuan Song
Sep 10, 2019·Multivariate Behavioral Research·Holger Brandt
Dec 12, 2019·Multivariate Behavioral Research·Rebecca D Büchner, Andreas G Klein
Jun 25, 2019·Frontiers in Psychology·Leslie A HaydukMatthias Hoben
Aug 12, 2021·Multivariate Behavioral Research·Zachary J Roman, Holger Brandt

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