Moderate-Deviation-Based Inference for Random Degeneration in Paired Rank Lists
研究当两个评估者对N个项目独立排名时，如何通过聚合排名列表并处理不一致性来推断随机退化，提出了基于非平稳伯努利试验的模型和算法，并用模拟和真实数据验证了其数值性质。
Consider a problem where N items (objects or individuals) are judged by assessors using their perceptions of a set of performance criteria, or alternatively by technical devices. In particular, two assessors might rank the items between 1 and N on the basis of relative performance, independently of each other. We can aggregate the rank lists by assigning one if the two assessors agree, and zero otherwise, and we can modify this approach to make it robust against irregularities. In this article, we consider methods and algorithms that can be used to address this problem. We study their theoretical properties in the case of a model based on nonstationary Bernoulli trials, and we report on their numerical properties for both simulated and real data.