标题： 两向“按段聚类”问题的离散潜在变量模型中的复合似然推断 显示英文标题

标题： Composite likelihood inference in a discrete latent variable model for two-way "clustering-by-segmentation" problems

摘要： 我们考虑一个用于二维数据数组的离散潜在变量模型，该模型可以同时在数据的一个维度上产生聚类（例如可交换的观测单元或特征）和在另一个维度上产生连续组或段（例如连续有序的时间或位置）。该模型依赖于隐马尔可夫结构，但由于其复杂性，无法通过完整最大似然进行估计。因此，我们引入基于考虑数据不同子集的复合似然方法。所提出的方法通过模拟进行说明，并应用于基因组数据。 显示英文摘要

摘要： We consider a discrete latent variable model for two-way data arrays, which allows one to simultaneously produce clusters along one of the data dimensions (e.g. exchangeable observational units or features) and contiguous groups, or segments, along the other (e.g. consecutively ordered times or locations). The model relies on a hidden Markov structure but, given its complexity, cannot be estimated by full maximum likelihood. We therefore introduce composite likelihood methodology based on considering different subsets of the data. The proposed approach is illustrated by simulation, and with an application to genomic data.