A fiducial approach to the nonparametric deconvolution problem:The discrete case  

作　　者：Yifan Cui Jan Hannig 

机构地区：[1]Center for Data Science,Zhejiang University,Hangzhou 310058,China [2]Department of Statistics and Operations Research,University of North Carolina at Chapel Hill,Chapel Hill,NC 27599,USA

出　　处：《Science China Mathematics》2024年第11期2653-2670,共18页中国科学（数学英文版）

基　　金：supported by National Natural Science Foundation of China(Grant No.U23A2064);Singapore Ministry of Education;U.S.National Institute of Health;U.S.National Science Foundation。

摘　　要：Fiducial inference is applied to nonparametric g-modeling in the discrete case.We propose a computationally efficient algorithm to sample from the fiducial distribution and use the generated samples to construct point estimates and confidence intervals.We study the theoretical properties of the fiducial distribution and perform extensive simulations in various scenarios.The proposed approach gives rise to good statistical performance in terms of the mean squared error of point estimators and coverage of confidence intervals.Furthermore,we apply the proposed fiducial method to estimate the probability of each satellite site being malignant using gastric adenocarcinoma data with 844 patients.

关 键 词：confidence intervals empirical Bayes fiducial inference nonparametric deconvolution 

分 类 号：O212.1[理学—概率论与数理统计]