Open Access
Translator Disclaimer
June, 1989 Asymptotic Properties of Statistical Estimators in Stochastic Programming
Alexander Shapiro
Ann. Statist. 17(2): 841-858 (June, 1989). DOI: 10.1214/aos/1176347146

Abstract

The aim of this article is to investigate the asymptotic behaviour of estimators of the optimal value and optimal solutions of a stochastic program. These estimators are closely related to the $M$-estimators introduced by Huber (1964). The parameter set of feasible solutions is supposed to be defined by a number of equality and inequality constraints. It will be shown that in the presence of inequality constraints the estimators are not asymptotically normal in general. Maximum likelihood and robust regression methods will be discussed as examples.

Citation

Download Citation

Alexander Shapiro. "Asymptotic Properties of Statistical Estimators in Stochastic Programming." Ann. Statist. 17 (2) 841 - 858, June, 1989. https://doi.org/10.1214/aos/1176347146

Information

Published: June, 1989
First available in Project Euclid: 12 April 2007

zbMATH: 0688.62025
MathSciNet: MR994271
Digital Object Identifier: 10.1214/aos/1176347146

Subjects:
Primary: 62F12
Secondary: 90C15

Rights: Copyright © 1989 Institute of Mathematical Statistics

JOURNAL ARTICLE
18 PAGES


SHARE
Vol.17 • No. 2 • June, 1989
Back to Top