Copula Gaussian graphical models with penalized ascent Monte Carlo EM algorithm

Fentaw Abegaz*, Ernst Wit

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)

Abstract

Typical data that arise from surveys, experiments, and observational studies include continuous and discrete variables. In this article, we study the interdependence among a mixed (continuous, count, ordered categorical, and binary) set of variables via graphical models. We propose an (1)-penalized extended rank likelihood with an ascent Monte Carlo expectation maximization approach for the copula Gaussian graphical models and establish near conditional independence relations and zero elements of a precision matrix. In particular, we focus on high-dimensional inference where the number of observations are in the same order or less than the number of variables under consideration. To illustrate how to infer networks for mixed variables through conditional independence, we consider two datasets: one in the area of sports and the other concerning breast cancer.

Original languageEnglish
Pages (from-to)419-441
Number of pages23
JournalStatistica Neerlandica
Volume69
Issue number4
DOIs
Publication statusPublished - Nov-2015

Keywords

  • Gaussian copula
  • (l)-penalized maximum likelihood
  • Gaussian graphical models
  • ascent-MCEM algorithm
  • extended rank likelihood
  • conditional independence
  • FOOTBALL-LEAGUE GAMES
  • BREAST-CANCER
  • COVARIANCE ESTIMATION
  • LIKELIHOOD-ESTIMATION
  • MAXIMUM-LIKELIHOOD
  • BCL-2 EXPRESSION
  • BINARY DATA
  • AMPLIFICATION
  • CONVERGENCE
  • ASSOCIATION

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