A ROBBINS-MONRO PROCEDURE FOR ESTIMATION IN SEMIPARAMETRIC REGRESSION MODELS

• 成果类型: Article
• 署名作者: Bercu, Bernard; Fraysse, Philippe
• 署名单位: Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Bordeaux
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/12-AOS969
• 发表日期: 2012
• 页码: 666-693
• 关键词: shape-invariant models; Nonparametric Regression; stochastic-approximation; nonlinear-regression; maximum-likelihood; iterated logarithm; CONVERGENCE; algorithms; LAW

摘要

This paper is devoted to the parametric estimation of a shift together with the nonparametric estimation of a regression function in a semiparametric regression model. We implement a very efficient and easy to handle Robbins-Monro procedure. On the one hand, we propose a stochastic algorithm similar to that of Robbins-Monro in order to estimate the shift parameter. A preliminary evaluation of the regression function is not necessary to estimate the shift parameter. On the other hand, we make use of a recursive Nadaraya-Watson estimator for the estimation of the regression function. This kernel estimator takes into account the previous estimation of the shift parameter. We establish the almost sure convergence for both Robbins-Monro and Nadaraya-Watson estimators. The asymptotic normality of our estimates is also provided. Finally, we illustrate our semiparametric estimation procedure on simulated and real data.