A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning.

ABSTRACT: Graphical modeling has been broadly useful for exploring the dependence structure among features in a dataset. However, the strength of graphical modeling hinges on our ability to encode and estimate conditional dependencies. In particular, commonly used measures such as partial correlation are only meaningful under strongly parametric (in this case, multivariate Gaussian) assumptions. These assumptions are unverifiable, and there is often little reason to believe they hold in practice. In this paper, we instead consider 3 nonparametric measures of conditional dependence. These measures are meaningful without structural assumptions on the multivariate distribution of the data. In addition, we show that for 2 of these measures there are simple, strong plug-in estimators that require only th

SUBMITTER: Xiang Y 

PROVIDER: S-EPMC7787692 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature