Gendre, Xavier Model selection and estimation of a component in additive regression. (2014) ESAIM: Probability and Statistics, 18. 77116. ISSN 12928100

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Official URL: https://doi.org/10.1051/ps/2012028
Abstract
Let $Y\in\R^n$ be a random vector with mean $s$ and covariance matrix $\sigma^2P_n\tra{P_n}$ where $P_n$ is some known $n\times n$matrix. We construct a statistical procedure to estimate $s$ as well as under moment condition on $Y$ or Gaussian hypothesis. Both cases are developed for known or unknown $\sigma^2$. Our approach is free from any prior assumption on $s$ and is based on nonasymptotic model selection methods. Given some linear spaces collection $\{S_m,\ m\in\M\}$, we consider, for any $m\in\M$, the leastsquares estimator $\hat{s}_m$ of $s$ in $S_m$. Considering a penalty function that is not linear to the dimensions of the $S_m$'s, we select some $\hat{m}\in\M$ in order to get an estimator $\hat{s}_{\hat{m}}$ with a quadratic risk as close as possible to the minimal one among the risks of the $\hat{s}_m$'s. Nonasymptotic oracletype inequalities and minimax convergence rates are proved for $\hat{s}_{\hat{m}}$. A special attention is given to the estimation of a nonparametric component in additive models. Finally, we carry out a simulation study in order to illustrate the performances of our estimators in practice.
Item Type:  Article 

HAL Id:  hal01597060 
Audience (journal):  International peerreviewed journal 
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Institution:  French research institutions > Centre National de la Recherche Scientifique  CNRS (FRANCE) Université de Toulouse > Université Toulouse III  Paul Sabatier  UT3 (FRANCE) Université de Toulouse > Université Toulouse  Jean Jaurès  UT2J (FRANCE) Université de Toulouse > Université Toulouse 1 Capitole  UT1 (FRANCE) Other partners > Institut National des Sciences Appliquées  INSA (FRANCE) 
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Deposited On:  22 Feb 2019 10:10 
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