2012/11/06 by Salvatore Ingrassia, Ingrassia, Salvatore, Simona C. Minotti +5
Computer Science · Mathematics · #Applications (stat.AP) #Bayesian Methods and Mixture Models #Computation (stat.CO) #Data Management and Algorithms #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.1211.1171
openalex publication_date 2012/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Cluster-Weighted Modeling (CWM) is a flexible mixture approach for modeling the joint probability of data coming from a heterogeneous population as a weighted sum of the products of marginal distributions and conditional distributions. In this paper, we introduce a wide family of Cluster Weighted models in which the conditional distributions are assumed to belong to the exponential family with canonical links which will be referred to as Generalized Linear Gaussian Cluster Weighted Models. Moreover, we show that, in a suitable sense, mixtures of generalized linear models can be considered as nested in Generalized Linear Gaussian Cluster Weighted Models. The proposal is illustrated through many numerical studies based on both simulated and real data sets.