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High dimensional semiparametric latent graphical model for mixed data

Author(s): Fan, Jianqing; Liu, Han; Ning, Yang; Zou, Hui

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Abstract: We propose a semiparametric latent Gaussian copula model for modelling mixed multivariate data, which contain a combination of both continuous and binary variables. The model assumes that the observed binary variables are obtained by dichotomizing latent variables that satisfy the Gaussian copula distribution. The goal is to infer the conditional independence relationship between the latent random variables, based on the observed mixed data. Our work has two main contributions: we propose a unified rank-based approach to estimate the correlation matrix of latent variables; we establish the concentration inequality of the proposed rank-based estimator. Consequently, our methods achieve the same rates of convergence for precision matrix estimation and graph recovery, as if the latent variables were observed. The methods proposed are numerically assessed through extensive simulation studies, and real data analysis.
Publication Date: Mar-2017
Electronic Publication Date: 5-Apr-2016
Citation: Fan, Jianqing, Liu, Han, Ning, Yang, Zou, Hui. (2017). High dimensional semiparametric latent graphical model for mixed data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79 (2), 405 - 421. doi:10.1111/rssb.12168
DOI: doi:10.1111/rssb.12168
ISSN: 1369-7412
Pages: 405 - 421
Type of Material: Journal Article
Journal/Proceeding Title: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Version: Author's manuscript

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