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. Copyright © Taylor & Francis Group, LLC.

Least Squares Consistent Estimates for Arbitrary Regression Functions over an Abstract Space

PARPINEL, Francesca
2012-01-01

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. Copyright © Taylor & Francis Group, LLC.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/23377
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