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A dimension reduction based approach for estimation and variable selection in partially linear single-index models with high-dimensional covariates
In this paper, we formulate the partially linear single-index models as bi-index dimension reduction models for the purpose of identifying significant covariates in both the linear part and the single-index part through only one combined index in a dimension reduction approach. This is different from all existing dimension reduction methods …