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Member Research & Reports

Member Research & Reports

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

A study co-authored by Dr. Yang Feng, associate professor of biostatistics at the New York University School of Global Public Health, was published in the Journal of Econometrics titled “A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models.” 

Rapid development in technology continuously floods us with high-dimensional data nowadays. One interesting question this study wishes to answer is their conditional dependency structure. 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. The study authors show the superiority of the new method, implemented in the R package pgraph, through simulation and real data studies.

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