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Journal Article

Semi-supervised clustering of time-dependent categorical sequences with application to discovering education-based life patterns

Authors

Publication date

08 Mar 2021

Summary

A new approach to the analysis of heterogeneous categorical sequences is proposed. The first-order Markov model is employed in a finite mixture setting with initial state and transition probabilities being expressed as functions of time. The expectation–maximization algorithm approach to parameter estimation is implemented in the presence of positive equivalence constraints that determine which observations must be placed in the same class in the solution. The proposed model is applied to a dataset from the British Household Panel Survey to evaluate the association between the education background and life outcomes of study participants. The analysis of the survey data reveals many interesting relationships between the level of education and major life events.

Published in

Statistical Modelling

DOI

https://doi.org/10.1177/1471082X21989170

ISSN

16

Subjects

Statistical Analysis, Education, and Life Course Analysis

Notes

Online Early

#547292


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