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Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations
Author(s) -
Anderson Gordon,
Farcomeni Alessio,
Pittau Maria Grazia,
Zelli Roberto
Publication year - 2019
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12312
Subject(s) - merge (version control) , markov chain , latent class model , markov model , markov process , cluster analysis , econometrics , computer science , statistics , mathematics , information retrieval
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.