The Annals of Statistics
- Ann. Statist.
- Volume 45, Number 2 (2017), 897-922.
Interaction pursuit in high-dimensional multi-response regression via distance correlation
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.
Ann. Statist., Volume 45, Number 2 (2017), 897-922.
Received: December 2015
First available in Project Euclid: 16 May 2017
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Kong, Yinfei; Li, Daoji; Fan, Yingying; Lv, Jinchi. Interaction pursuit in high-dimensional multi-response regression via distance correlation. Ann. Statist. 45 (2017), no. 2, 897--922. doi:10.1214/16-AOS1474. https://projecteuclid.org/euclid.aos/1494921961
- Supplementary material to “Interaction pursuit in high-dimensional multi-response regression via distance correlation”. Due to space constraints, the details about the post-screening interaction selection, additional numerical studies, some intermediate steps of the proof of Theorem 1 and additional technical details are provided in the Supplementary Material .