Joint analysis of semicontinuous data with latent variables
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AbstractA two-part latent variable model is proposed to analyze semicontinuous data in the presence of latent variables. The proposed model comprises two major components. The first component is a structural equation model (SEM), which characterizes latent variables using corresponding multiple attributes and examines the interrelationships among them. The second component is a two-part model to assess a semicontinuous response of interest. The semicontinuous variable is characterized by a mixture of zero values and continuously distributed positive values. The two-part model manages this semicontinuous variable by splitting it into two random variables; one is a binary indicator to determine whether the response is zero, another is a continuous variable to determine the actual level of the positive response. A full Bayesian approach coupled with spike-and-slab lasso prior is developed for simultaneous variable selection and parameter estimation. The proposed methodology is demonstrated by a simulation study and applied to the analysis of the Chinese General Social Survey dataset. New insights into the interrelationships among non-cognitive ability, education level, and annual income are obtained.
All Author(s) ListWang X. Q., Feng X. N., Song X. Y.
Journal nameComputational Statistics and Data Analysis
Year2020
Month11
Volume Number151
PublisherElsevier
Article number107005
ISSN0167-9473
eISSN1872-7352
LanguagesEnglish-United States

Last updated on 2020-26-10 at 00:08