Asymptotically honest confidence sets for structural errors-in-variables models
成果类型：
Article
署名作者：
Huwang, L
署名单位：
Cornell University
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
发表日期：
1996
页码：
1536-1546
关键词：
摘要：
The problem of constructing confidence sets for the structural errors-in-variables model is considered under the assumption that the variance of the error associated with the covariate is known. Previously proposed confidence sets for this model suffer from the problem that they all have zero confidence levels for any sample size, where the confidence level of a confidence set is defined to be the infimum of coverage probability over the parameter space. In this paper we construct some asymptotically honest confidence sets; that is, the limiting values of their confidence levels are at least as large as the nominal probabilities when the sample size goes to infinity. A desirable property of the proposed confidence set for the slope is also established.