A Stepwise Approach for High-Dimensional Gaussian Graphical Models

Ruben H. Zamar, Marcelo Ruiz, Ginette Lafit, Javier Nogales

Type: Article

Publication Date: 2021-09-30

Citations: 2

DOI: https://doi.org/10.52933/jdssv.v1i2.11

Abstract

We present a stepwise approach to estimate high dimensional Gaussian graphicalmodels. We exploit the relation between the partial correlation coefficientsand the distribution of the prediction errors, and parametrize the model in termsof the Pearson correlation coefficients between the prediction errors of the nodes’best linear predictors. We propose a novel stepwise algorithm for detecting pairsof conditionally dependent variables. We compare the proposed algorithm withexisting methods including graphical lasso (Glasso), constrained `l1-minimization(CLIME) and equivalent partial correlation (EPC), via simulation studies andreal life applications. In our simulation study we consider several model settingsand report the results using different performance measures that look at desirablefeatures of the recovered graph.

Locations

Journal of Data Science Statistics and Visualisation - View - PDF
arXiv (Cornell University) - View - PDF
Lirias (KU Leuven) - View - PDF

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+	Asymptotic normality and optimalities in estimation of large Gaussian graphical models	2015	Zhao Ren Tingni Sun Cun‐Hui Zhang Harrison H. Zhou
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+ PDF Chat	Partial Correlation Estimation by Joint Sparse Regression Models	2009	Jie Peng Pei Wang Nengfeng Zhou Ji Zhu
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