2006/10/31 by Abdelkader Mokkadem, Mariane Pelletier · 2 citations
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Statistical Methods and Inference #Stochastic Gradient Optimization Techniques #math.ST #msc:62G08 #msc:62L20 #stat.TH
paper · pdf · doi:10.1214/009053606000001451
published as Annals of Statistics 2007, Vol. 35, No. 4, 1749-1772 · Published in at http://dx.doi.org/10.1214/009053606000001451 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2007/08/01 · arxiv created 2007/10/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A stochastic algorithm for the recursive approximation of the location θ of a maximum of a regression function was introduced by Kiefer and Wolfowitz [Ann. Math. Statist. 23 (1952) 462–466] in the univariate framework, and by Blum [Ann. Math. Statist. 25 (1954) 737–744] in the multivariate case. The aim of this paper is to provide a companion algorithm to the Kiefer–Wolfowitz–Blum algorithm, which allows one to simultaneously recursively approximate the size μ of the maximum of the regression function. A precise study of the joint weak convergence rate of both algorithms is given; it turns out that, unlike the location of the maximum, the size of the maximum can be approximated by an algorithm which converges at the parametric rate. Moreover, averaging leads to an asymptotically efficient algorithm for the approximation of the couple (θ,μ).