题目：Nonparametric Variable Screening for Multivariate Additive Models
报告人：张日权 教授（华东师范大学 统计学院院长）
【摘要】We propose a novel approach for variable screening in sparse multivariate additive models with random effects by use of null-beamforming on the data. The new approach includes two stages. In Stage 1, we approximate each nonparametric component by a linear combination of spline basis functions. Consequently, we convert the above problem to that of selecting groups of coefficients in a multivariate regression model with vector-valued covariates. In Stage 2, we conduct a series of filtering operations (called beamforming) by projections of the multiple response observations into each 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. The proposed procedure is shown to perform very well on simulated and real data. An asymptotic theory on the selection consistency has been established under some regularity conditions.
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