On the asymptotics of constrained local M-estimators
成果类型：
Article
署名作者：
Shapiro, A
署名单位：
University System of Georgia; Georgia Institute of Technology
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/aos/1015952006
发表日期：
2000
页码：
948-960
关键词：
摘要：
We discuss in this paper asymptotics of locally optimal solutions of maximum likelihood and, more generally, M-estimation procedures in cases where the true value of the parameter vector lies on the boundary of the parameter set S. We give a counterexample showing that regularity of S in the sense of Clarke is not; sufficient for asymptotic equivalence of rootn-consistent locally optimal M-estimators. We argue further that stronger properties, such as so-called near convexity or prox-regularity of S are required in order to ensure that any two rootn-consistent locally optimal M-estimators have the same asymptotics.