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


RIS


BibTeX

Abstract

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.

Tags

Press 'enter' for creating the tag

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

Associations

People
Frühwirth-Schnatter, Sylvia (Details)
External
Pamminger, Christoph
Organization
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search