Statistical Modelling 17 (6) (2017), 381–400

An application of Dirichlet process in clustering subjects via variance shift models: A course-evaluation study

Reyhaneh Rikhtehgaran
Department of Mathematical Sciences,
Isfahan University of Technology,
Isfahan, Iran
e-mail: r_rikhtehgaran@cc.iut.ac.ir

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.

Keywords:

course-evaluation data; Dirichlet process prior; Markov chain Monte Carlo; Model-based clustering; transition mixed-effects models; variance shift models.

Downloads:

The code for Section 6 can be found in the zipped archive. The real data used for this article are confidential. For any questions regarding this matter please contact the author.
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