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• W2964150627 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." @default.
• W2964150627 created "2019-07-30" @default.
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• W2964150627 date "2017-04-01" @default.
• W2964150627 modified "2023-10-10" @default.
• W2964150627 title "Interaction pursuit in high-dimensional multi-response regression via distance correlation" @default.
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• W2964150627 doi "https://doi.org/10.1214/16-aos1474" @default.
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