Article ID Journal Published Year Pages File Type
1149653 Journal of Statistical Planning and Inference 2012 12 Pages PDF
Abstract
In this paper we consider a semiparametric regression model involving a d-dimensional quantitative explanatory variable X and including a dimension reduction of X via an index β′X. In this model, the main goal is to estimate the Euclidean parameter β and to predict the real response variable Y conditionally to X. Our approach is based on sliced inverse regression (SIR) method and optimal quantization in Lp-norm. We obtain the convergence of the proposed estimators of β and of the conditional distribution. Simulation studies show the good numerical behavior of the proposed estimators for finite sample size.
Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
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