In this article, we propose a new approach to sieve estimation for a general regression function when the dimension of the finite dimensional subspaces is a random quantity depending on the values of the observations. The technique is introduced with the help of a simulation study on a functional linear model under extremely mild assumptions. A sketch of the proof concerning the main statements is then given in the more general case when the regression function is not necessarily linear.
Least Squares Consistent Estimates for Arbitrary Regression Functions Over an Abstract Space
FIORIN, SILVANO;
2012
Abstract
In this article, we propose a new approach to sieve estimation for a general regression function when the dimension of the finite dimensional subspaces is a random quantity depending on the values of the observations. The technique is introduced with the help of a simulation study on a functional linear model under extremely mild assumptions. A sketch of the proof concerning the main statements is then given in the more general case when the regression function is not necessarily linear.File in questo prodotto:
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