论文标题
依赖的潜在类模型
Dependent Latent Class Models
论文作者
论文摘要
潜在类模型（LCM）用于聚集多元分类数据（例如，基于调查响应的组参与者）。传统LCMS假定一个称为条件独立性的属性。该假设可以是限制性的，导致模型错误指定和过度参数化。为了解决这个问题，我们开发了一种新型的贝叶斯模型，称为依赖潜伏类模型（DLCM），该模型允许有条件的依赖性。我们验证DLCM的可识别性。我们还证明了DLCM在模拟和现实世界应用中的有效性。与传统的LCM相比，DLCM在时间序列，重叠项目和结构零的应用中有效。
Latent Class Models (LCMs) are used to cluster multivariate categorical data (e.g. group participants based on survey responses). Traditional LCMs assume a property called conditional independence. This assumption can be restrictive, leading to model misspecification and overparameterization. To combat this problem, we developed a novel Bayesian model called a Dependent Latent Class Model (DLCM), which permits conditional dependence. We verify identifiability of DLCMs. We also demonstrate the effectiveness of DLCMs in both simulations and real-world applications. Compared to traditional LCMs, DLCMs are effective in applications with time series, overlapping items, and structural zeroes.
