A Robbins–Monro procedure for estimation in semiparametric regression models

The Annals of Statistics · 2012 · Volume 40 · Issue 2

Abstract

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.

Keywords

62G05, 62G20, asymptotic properties, estimation of a regression function, estimation of a shift, Semiparametric estimation

Enable JavaScript for citation tools and additional paper interactions.