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Joint analysis of semicontinuous data with latent variables

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  • Wang, Xiaoqing
  • Feng, Xiangnan
  • Song, Xinyuan

Abstract

A 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.

Suggested Citation

  • Wang, Xiaoqing & Feng, Xiangnan & Song, Xinyuan, 2020. "Joint analysis of semicontinuous data with latent variables," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:csdana:v:151:y:2020:i:c:s0167947320300967
    DOI: 10.1016/j.csda.2020.107005
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