Rectangular latent Markov models for time-specific clustering, with an analysis of the wellbeing of nations

A latent Markov model admitting variation in the number of latent states at each time period is introduced. The model facilitates subjects switching latent states at each time period according to an inhomogeneous first-order Markov process, wherein transition matrices are generally rectangular. As a...

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Bibliographic Details
Published inJournal of the Royal Statistical Society Series C: Applied Statistics Vol. 68; no. 3; pp. 603 - 621
Main Authors Anderson, Gordon, Farcomeni, Alessio, Pittau, Maria Grazia, Zelli, Roberto
Format Journal Article
LanguageEnglish
Published Oxford Wiley 01.04.2019
Oxford University Press
Subjects
Clustering
Group merging
Group splitting
Human development
Human development index
Latent transitions
Markov analysis
Markov chains
Matrices
Well being
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Summary:A latent Markov model admitting variation in the number of latent states at each time period is introduced. The model facilitates subjects switching latent states at each time period according to an inhomogeneous first-order Markov process, wherein transition matrices are generally rectangular. As a consequence, latent groups can merge, split or be rearranged. An application analysing the progress of wellbeing of nations, as measured by the three dimensions of the human development index over the last 25 years, illustrates the approach.
ISSN:0035-9254
1467-9876
DOI:10.1111/rssc.12312