Partially Linear Reduced-rank Regression

نویسندگان

  • K. S. Chan
  • Ming-Chung Li
چکیده

We introduce a new dimension-reduction technique, the Partially Linear Reduced-rank Regression (PLRR) model, for exploring possible nonlinear structure in a regression involving both multivariate response and covariate. The PLRR model specifies that the response vector loads linearly on some linear indices of the covariate, and nonlinearly on some other indices of the covariate. We give a set of sufficient conditions for the identifiability of the PLRR model. We propose a method for estimating a PLRR model, and derive the large-sample properties of the estimator. Simulation and real data analysis are used to illustrate the new approach.

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تاریخ انتشار 2004