标题： 分布回归中最大似然估计的渐近性质在随机删失下 显示英文标题

标题： Asymptotic properties of the MLE in distributional regression under random censoring

摘要： 分布回归的目的是在给定的条件分布参数族中找到最佳候选分布来建模给定的数据集。 由于分布族中的每个候选分布都可以通过相应的分布参数来识别，因此该任务的一种常见方法是使用最大似然估计量（MLE）来估计参数。 在本文中，我们建立了在响应变量受到随机右删失情况下该估计量的理论结果。 特别是，在删失条件下，我们提供了MLE几乎必然一致性和渐近正态性的证明。 此外，通过一个模拟研究对有限样本行为进行了示例演示。 显示英文摘要

摘要： The aim of distributional regression is to find the best candidate in a given parametric family of conditional distributions to model a given dataset. As each candidate in the distribution family can be identified by the corresponding distribution parameters, a common approach for this task is using the maximum likelihood estimator (MLE) for the parameters. In this paper, we establish theoretical results for this estimator in case the response variable is subject to random right censoring. In particular, we provide proofs of almost sure consistency and asymptotic normality of the MLE under censoring. Further, the finite-sample behavior is exemplarily demonstrated in a simulation study.