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Testing for Regression Heteroskedasticity with High-Dimensional Random Forests

by   Chi Chien-Ming, et al.
University of Florida

Statistical inference for high-dimensional regression heteroskedasticity is an important but under-explored problem. The current paper aims at filling this gap by proposing two tests, namely the variance difference test and the variance difference Breusch-Pagan test, for assessing high-dimensional regression heteroskedasticity. The former tests whether an explanatory feature of interest is associated with the conditional variance of a response variable, while the latter tests heteroskedasticity in the regression, which is known to be the Breusch-Pagan test problem. To formally establish the tests, we have derived rigorous P-values and test sizes, and analyzed the test power under a nonparametric heteroskedastic data generating model with high-dimensional input features. Such a model setting takes into account high-dimensional applications with flexible structures of heteroskedasticity and features having interaction effects on the mean of the response; these are common applications in many fields such as biology. Our methods leverage machine learning mean prediction methods such as random forests and use knockoff variables as negative controls. Particularly, the definition of knockoffs for our test statistics is more flexible than the original definition of knockoffs, and we give a detailed comparison of these two definitions and discuss the advantages of our knockoffs. The satisfactory empirical performance of the proposed tests is illustrated with simulation results and an HIV (Human Immunodeficiency Virus) case study.


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