Fiducial inference, as generalized by Hannig et al. (2016), is applied to nonparametric g-modeling (Efron, 2016) 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 yields good statistical performance in terms of the mean squared error of point estimators and the 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 (Efron, 2016).

翻译：由Hannig等人(2016年)普遍采用的纤维推论适用于离散情况下的非参数性g建模(Efron,2016年),我们建议从分布区取样时采用计算效率高的算法,并利用生成的样本来建立点估计和信任间隔;我们研究分布的理论特性,并在各种假设中进行广泛的模拟;拟议方法在点测算器的平均正方差和信任间隔的覆盖面方面产生良好的统计性能;此外,我们采用拟议的纤维推算法,利用844名病人的气态蛋白癌数据估计每个卫星站点恶性的可能性(Efron,2016年)。