A Projection-based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models.

Journal of Econometrics
Jianqing FanLucy Xia

Abstract

Measuring conditional dependence is an important topic in econometrics with broad applications including graphical models. Under a factor model setting, a new conditional dependence measure based on projection is proposed. The corresponding conditional independence test is developed with the asymptotic null distribution unveiled where the number of factors could be high-dimensional. It is also shown that the new test has control over the asymptotic type I error and can be calculated efficiently. A generic method for building dependency graphs without Gaussian assumption using the new test is elaborated. We show the superiority of the new method, implemented in the R package pgraph, through simulation and real data studies.

References

Dec 15, 2007·Biostatistics·Jerome FriedmanRobert Tibshirani
Jan 27, 2012·Journal of the American Statistical Association·Jianqing FanRui Song
Jul 12, 2014·Biometrika·Arend VoormanDaniela Witten
Feb 16, 2016·Journal of the American Statistical Association·Xueqin WangHeping Zhang
Mar 1, 2015·Biometrika·Shizhe ChenAli Shojaie
May 10, 2017·Journal of the Royal Statistical Society. Series B, Statistical Methodology·Jianqing FanYuyan Wang

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