An application of Dirichlet process in clustering subjects via variance shift models: A course-evaluation study. (December 2017)

• Record Type: Journal Article
• Authors: Rikhtehgaran, Reyhaneh
• Is Part Of: Statistical modelling. Volume 17:Number 6(2017)
• Page Start: 381
• Page End: 400
• Publication Date: 2017-12
• Subjects: course-evaluation data -- Dirichlet process prior -- Markov chain Monte Carlo -- Model-based clustering -- transition mixed-effects models -- variance shift models; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X17699299
• Languages: English
• ISSNs: 1471-082X

Abstract

In this article, the Dirichlet process (DP) is applied to cluster subjects with longitudinal observations. The basis of clustering is the ability of subjects to adapt themselves to new circumstances. Indeed, the basis of clustering depends on the time of changing response variability. This is done by providing a random change-point time in the variance structure of mixed-effects models. The DP is assumed as a prior for the distribution of the random change point. The discrete nature of the DP is utilized to cluster subjects according to the time of adaption. The proposed model is useful to identify groups of subjects with distinctive time-based progressions or declines. Transition mixed-effects models are also used to account for the serial correlation among observations over time. A joint modelling approach is utilized to handle the bias created in these models. The Gibbs sampling technique is adopted to achieve parameter estimates. Performance of the proposed method is evaluated via conducting a simulation study. The usefulness of the proposed model is assessed on a course-evaluation dataset.