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Selection of variables and dimension reduction in high-dimensional non-parametric regression
dimension reduction high dimension LASSO
2009/9/16
We consider a $l_1$-penalization procedure in the non-parametric Gaussian regression model. In many concrete examples, the dimension $d$ of the input variable $X$ is very large (sometimes depending on...
Theoretical properties of Cook's PFC dimension reduction algorithm for linear regression
Principal Components Principal Fitted Components random matrix theory regression
2009/9/16
We analyse the properties of the Principal Fitted Components (PFC) algorithm proposed by Cook. We derive theoretical properties of the resulting estimators, including sufficient conditions under which...