论文标题
来自排名模型的非凡属性
New algorithms and goodness-of-fit diagnostics from remarkable properties of ranking models
论文作者
论文摘要
远期顺序假设假设项目的排名过程是通过将位置从顶部（最喜欢）分配到底部（最不喜欢的）替代方案来实现的。最近，通过引入离散参考顺序参数，用扩展的plackett-luce模型（EPL）放松了这个假设，描述了等级归属路径。通过从EPL的两个形式属性开始，前者与排名过程的第一和最后阶段的项目概率的反相反，而后者众所周知是无关的替代方案的独立性（或Luce's Choice Axiom），我们得出了新颖的诊断工具，用于测试实际采样分布的eplaptiments opriaptionals opriaptiations of the Samples sampling等级的分布。除了填补多阶段模型家族的拟合优点方法的空白外，我们还展示了如何方便利用这两个统计数据之一来构建一种启发式方法，从而代理推断基础参考顺序参数的最大似然方法。与更常规的方法相比，提案的相对性能通过广泛的模拟研究说明。
The forward order assumption postulates that the ranking process of the items is carried out by sequentially assigning the positions from the top (most-liked) to the bottom (least-liked) alternative. This assumption has been recently relaxed with the Extended Plackett-Luce model (EPL) through the introduction of the discrete reference order parameter, describing the rank attribution path. By starting from two formal properties of the EPL, the former related to the inverse ordering of the item probabilities at the first and last stage of the ranking process and the latter well-known as independence of irrelevant alternatives (or Luce's choice axiom), we derive novel diagnostic tools for testing the appropriateness of the EPL assumption as the actual sampling distribution of the observed rankings. Besides contributing to fill the gap of goodness-of-fit methods for the family of multistage models, we also show how one of the two statistics can be conveniently exploited to construct a heuristic method, that surrogates the maximum likelihood approach for inferring the underlying reference order parameter. The relative performance of the proposals compared with more conventional approaches is illustrated by means of extensive simulation studies.
