Asymptotic Expansions for Confidence Limits in the Presence of Nuisance Parameters, with Applications
研究了存在干扰参数时基于一致估计量的置信限的级数展开，以极大似然估计为例推导了抽样分布特征，并应用于威布尔分布尺度参数和形状参数的置信限设定，与模拟研究进行了比较。
SUMMARY Series expansions for confidence limits based on consistent estimators are obtained in the presence of nuisance parameters. The maximum likelihood estimator is treated as a special case and the necessary characteristics of its sampling distribution are obtained in terms of basic quantities derivable from the likelihood function. The theory is applied to the problem of setting separate confidence limits for the scale and shape parameters of the Weibull distribution. Comparisons are made with earlier simulation studies.