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 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 surprisingly 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 (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),我们建议采用一种计算高效的算法,从扇形分布中取样,并利用生成的样本来建立点估计和信任间隔;我们研究纤维分布的理论特性,并在各种假设中进行广泛的模拟;拟议方法在点估计器的平均正方差和信任间隔的覆盖面方面产生了令人惊讶的良好统计性能;此外,我们采用拟议的纤维法,利用与844名病人的胃肾上腺癌数据估计每个卫星站点的恶性概率(Efron,2016年)。