Displaying similar documents to “Model selection for (auto-)regression with dependent data”

Model selection for regression on a random design

Yannick Baraud (2002)

ESAIM: Probability and Statistics

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We consider the problem of estimating an unknown regression function when the design is random with values in ℝ k . Our estimation procedure is based on model selection and does not rely on any prior information on the target function. We start with a collection of linear functional spaces and build, on a data selected space among this collection, the least-squares estimator. We study the performance of an estimator which is obtained by modifying this least-squares estimator on a set of...

Sensitivity analysis of M -estimators of non-linear regression models

Asunción Rubio, Francisco Quintana, Jan Ámos Víšek (1994)

Commentationes Mathematicae Universitatis Carolinae

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An asymptotic formula for the difference of the M -estimates of the regression coefficients of the non-linear model for all n observations and for n - 1 observations is presented under conditions covering the twice absolutely continuous ϱ -functions. Then the implications for the M -estimation of the regression model are discussed.

Histogram selection in non Gaussian regression

Marie Sauvé (2009)

ESAIM: Probability and Statistics

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We deal with the problem of choosing a piecewise constant estimator of a regression function mapping 𝒳 into ℝ . We consider a non Gaussian regression framework with deterministic design points, and we adopt the non asymptotic approach of model selection penalization developed by Birgé and Massart. Given a collection of partitions of 𝒳 , with possibly exponential complexity, and the corresponding collection of piecewise constant estimators, we propose a penalized least squares criterion...