A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models
Methodology
2019-01-14 v5 Statistics Theory
Applications
Machine Learning
Statistics Theory
Abstract
Measuring conditional dependence is an important topic in statistics 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 significance level and can be calculated efficiently. A generic method for building dependency graphs without Gaussian assumption using the new test is elaborated. Numerical results and real data analysis show the superiority of the new method.
Cite
@article{arxiv.1501.01617,
title = {A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models},
author = {Jianqing Fan and Yang Feng and Lucy Xia},
journal= {arXiv preprint arXiv:1501.01617},
year = {2019}
}
Comments
39 pages, 5 figures