Quotation Pamminger, Christoph, Frühwirth-Schnatter, Sylvia. 2010. Model-based clustering of categorical time series. Bayesian Analysis. 5 (2), 345-368.




Two approaches for model-based clustering of categorical time series based on time-homogeneous first-order Markov chains are discussed. For Markov chain clustering the individual transition probabilities are fixed to a group-specific transition matrix. In a new approach called Dirichlet multinomial clustering the rows of the individual transition matrices deviate from the group mean and follow a Dirichlet distribution with unknown group-specific hyperparameters. Estimation is carried out through Markov chain Monte Carlo. Various well-known clustering criteria are applied to select the number of groups. An application to a panel of Austrian wage mobility data leads to an interesting segmentation of the Austrian labor market.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Bayesian Analysis
WU-Journal-Rating new FIN-A
Language English
Title Model-based clustering of categorical time series
Volume 5
Number 2
Year 2010
Page from 345
Page to 368
URL https://projecteuclid.org/euclid.ba/1340218342
DOI http://dx.doi.org/10.1214/10-BA606
Open Access Y
Open Access Link https://projecteuclid.org/euclid.ba/1340218342


Frühwirth-Schnatter, Sylvia (Details)
Pamminger, Christoph
Institute for Statistics and Mathematics IN (Details)
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