Abstract
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. We study the asymptotic behavior of the statistical estimators that maximize a not necessarily differentiable criterion function, possibly subject to side constraints (equalities and inequalities). Thn consistency results gener-alize those of Wald and Huber. Conditions are also given under which one is still able to obtain asymptotic normality. The analysis brings to the fore the relationship between the problem of finding statistical estimators and that of finding the optimal solutions of stochastic optimization problems with partial information. The last section is devoted to the properties of the saddle points of the associated Lagrangians.
Cite
CITATION STYLE
Dupacova, J., & Wets, R. (2007). Asymptotic Behavior of Statistical Estimators and of Optimal Solutions of Stochastic Optimization Problems. The Annals of Statistics, 16(4). https://doi.org/10.1214/aos/1176351052
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.