论文标题
分类数据的对数线性模型的复合混合物
Composite mixture of log-linear models for categorical data
论文作者
论文摘要
多元分类数据通常在许多应用领域收集。随着表中的细胞数量随变量的数量呈指数增长，许多甚至大多数细胞都会包含零观测值。这种严重的稀疏性激发了适当的统计方法，这些方法论有效地减少了自由参数的数量，受惩罚的对数线性模型和潜在结构分析是流行的选择。本文提出了一种从根本上进行新的方法，我们称之为日志线性模型（工厂）的混合物。米尔斯结合了潜在的类分析和对数线性模型，定义了一种新型的贝叶斯方法，以模拟复杂的多元分类和灵活性和解释性。磨坊比在模拟中的应急表和调查自杀企图和同理心之间关系的申请中的替代方法具有关键优势。
Multivariate categorical data are routinely collected in many application areas. As the number of cells in the table grows exponentially with the number of variables, many or even most cells will contain zero observations. This severe sparsity motivates appropriate statistical methodologies that effectively reduce the number of free parameters, with penalized log-linear models and latent structure analysis being popular options. This article proposes a fundamentally new class of methods, which we refer to as Mixture of Log Linear models (mills). Combining latent class analysis and log-linear models, mills defines a novel Bayesian methodology to model complex multivariate categorical with flexibility and interpretability. Mills is shown to have key advantages over alternative methods for contingency tables in simulations and an application investigating the relation among suicide attempts and empathy.
