NONPARAMETRIC CONDITIONAL INFERENCE FOR A LOCATION PARAMETER
成果类型：
Article
署名作者：
SEVERINI, TA
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
发表日期：
1994
页码：
353-362
关键词：
摘要：
Let Y1, . . ., Y(n) denote independent observations of the form Y(j) = theta + sigmaepsilon(j) where epsilone1, . . ., epsilon(n) are independent random variables each distributed according to a density p, and theta and sigma are unknown parameters. This paper presents a method for conditional inference about the location parameter theta that does not require the specification of a parametric family of densities for p. This method uses the configuration statistic to select the density p and is based on the work of Fraser.