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GRID for model structure discovering in high dimensional regression
Given a nonparametric regression model, we assume that the number of covariates d → ∞ but only some of these covariates are relevant for the model. Our goal is to identify the relevant covariates and to obtain some ...
Bias-corrected inference for multivariate nonparametric regression: model selection and oracle property
The local polynomial estimator is particularly affected by the curse of di- mensionality. So, the potentialities of such a tool become ineffective for large dimensional applications. Motivated by this, we propose a new ...
A locally adaptive bandwidth selector for kernel based regression
The selection of the smoothing parameter represents a crucial step in the local polynomial regression, because of the implications on the consistency of the nonparametric regression estimator and because of the difficulties ...