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Nonparametric Variable Screening for Multivariate Additive Models

Ding, Hui, Zhang, Jian, Zhang, Riquan (2018) Nonparametric Variable Screening for Multivariate Additive Models. TBD, . (Submitted)

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Abstract

We propose a novel approach to nonparametric variable screening for sparse multivariate additive

models with random effects, which includes two stages. In Stage 1, each nonparametric component is approximated by a linear combination of spline basis functions. Under this approximation, the above screening problem can be treated as selecting block-matrices of regression coefficients for a multivariate regression model. In Stage 2, a series of filtering operations are conducted by projections of the multiple response observations into the covariate space; each filter is tailored to a particular covariate and resistant to interferences originating from other covariates and from background

noises. The filtering is further improved by sequentially nulling significant covariates detected in the previous steps. An asymptotic theory on the selection consistency has been established under some regularity conditions. By simulations, the proposed procedure is shown to outperform the existing procedures in terms of sensitivity and specificity over a wide range of scenarios. We apply the proposed approach to the integrative analysis of the anti-cancer drug data, identifying a few biomarkers that potentially influence the concentration of drugs in cancer cell lines.

Item Type: Article
Uncontrolled keywords: Multivariate additive models, high-dimensional multivariate data, nonparametric variable screening and beamforming
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 14 Apr 2018 11:10 UTC
Last Modified: 23 Aug 2019 10:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/66718 (The current URI for this page, for reference purposes)
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