Model-based clustering of categorical time series
By Christoph Pamminger, Sylvia Frühwirth-Schnatter. 2010
64 citations recorded by the source corpus.
Two approaches for model-based clustering of categorical time series based on time-homogeneous flrst-order Markov chains are discussed. For Markov chain clustering the individual transition probabilities are flxed to a group-speciflc 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-speciflc 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.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/942124175e9233268d5e41e9e3e754686fb1eac5
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In Artificial Intelligence
- Isobel Claire Gormley
- Sylvia Frühwirth-Schnatter
- S. Kaufmann
- Anna Pajor
- Jeroen K. Vermunt
- Kerrie L. Mengersen
- Judith Rousseau
- David Ardia
In Signal Processing
Citations in the corpus
- Mixtures of Experts Models (2018, background): “Model M 1 is a standard finite mixture of time-homogeneous Markov chain models of order one (Pamminger & Frühwirth-Schnatter, 2010) where each component-specific density f g ( · ) in (1) is characterized by a transition matrix ξ g with J = 3 rows and the weight distribution η 1 , . . . , η G is…”