Model-based clustering of categorical time series. (English) Zbl 1330.62256
Summary: 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.
MSC:
62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
65C60 | Computational problems in statistics (MSC2010) |
60J22 | Computational methods in Markov chains |
62P20 | Applications of statistics to economics |
91B82 | Statistical methods; economic indices and measures |