期刊
BIOMETRIKA
卷 93, 期 4, 页码 973-987出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/93.4.973
关键词
cumulant; location-scale estimation; M-estimation; Monte Carlo simulation; one-sided confidence limit; one-sided p-value; profile likelihood; nuisance parameter; null hypothesis bootstrap; signed root statistic
This paper presents a simple method of calculating one-sided p-values which yield accurate inferences for a simple null hypothesis or, equivalently, accurate one-sided confidence limits regarding a scalar parameter in the presence of nuisance parameters. The method, which extends the work of DiCiccio et al. (2001) and Lee & Young (2005) in the context of adjusted likelihood estimation, is based on a test statistic analogous to the signed root of the loglikelihood, but derived from the objective function of an M-estimator from a certain class. Monte Carlo simulation is used to avoid the need for onerous analytical calculations typical of competing procedures based on Edgeworth or saddle-point approximations. The specific class of M-estimators under consideration is characterised by the requirement that the associated test statistic has constant variance to second order. This class contains, among others, the maximum likelihood estimator as well as variants of commonly used M-estimators of location, such as Huber's (1964) Proposal 2.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据