Interaction Pursuit In High-Dimensional Multi-Response Regression Via Distance Correlation

Keywords

Distance correlation; High dimensionality; Interaction pursuit; Multiresponse regression; Sparsity; Square transformation

Abstract

Feature interactions can contribute to a large proportion of variation in many prediction models. In the era of big data, the coexistence of high dimensionality in both responses and covariates poses unprecedented challenges in identifying important interactions. In this paper, we suggest a two-stage interaction identification method, called the interaction pursuit via distance correlation (IPDC), in the setting of high-dimensional multi-response interaction models that exploits feature screening applied to transformed variables with distance correlation followed by feature selection. Such a procedure is computationally efficient, generally applicable beyond the heredity assumption, and effective even when the number of responses diverges with the sample size. Under mild regularity conditions, we show that this method enjoys nice theoretical properties including the sure screening property, support union recovery and oracle inequalities in prediction and estimation for both interactions and main effects. The advantages of our method are supported by several simulation studies and real data analysis.

Publication Date

4-1-2017

Publication Title

Annals of Statistics

Volume

45

Issue

2

Number of Pages

897-922

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1214/16-AOS1474

Socpus ID

85019721733 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85019721733

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