论文标题
关于木棍二项式模型中引导不确定性估计的有效性
On the validity of bootstrap uncertainty estimates in the Mallows-Binomial model
论文作者
论文摘要
木棍丁基分布是排名和评级的第一个联合统计模型（Pearce and Erosheva，2022）。由于对模型参数及其不确定性的频繁估计是具有挑战性的，因此自然要考虑非参数bootstrap。但是，尚不清楚非参数引导程序在这种情况下渐近有效。这是因为Mallows-Binomial模型通过连续数量的参数化，其离散顺序会影响可能性。在本说明中，我们证明了摩洛斯二元模型中最大似然估计值的自举不确定性在渐近上有效。
The Mallows-Binomial distribution is the first joint statistical model for rankings and ratings (Pearce and Erosheva, 2022). Because frequentist estimation of the model parameters and their uncertainty is challenging, it is natural to consider the nonparametric bootstrap. However, it is not clear that the nonparametric bootstrap is asymptotically valid in this setting. This is because the Mallows-Binomial model is parameterized by continuous quantities whose discrete order affects the likelihood. In this note, we demonstrate that bootstrap uncertainty of the maximum likelihood estimates in the Mallows-Binomial model are asymptotically valid.
