Title: Clustering Longitudinal Life-Course Sequences Using Mixtures of Exponential-Distance Models
Speaker: Dr Keefe Murphy, Maynooth University
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Sequence analysis is an increasingly popular approach for the analysis of life courses represented by an ordered collection of activities experienced by subjects over a given time period. Several criteria exist for measuring pairwise dissimilarities among sequences. Typically, dissimilarity matrices are employed as input to heuristic clustering algorithms, with the aim of identifying the most relevant patterns in the data.
Here, we consider a survey data set containing information on the career trajectories of a cohort of Northern Irish youths tracked between the ages of 16 and 22. We propose an alternative clustering approach, suited to the analysis of such categorical sequence data from a holistic perspective, which is both model-based and distance-based. Our approach models the sequences themselves directly and employs distance metrics based on weighted variants of the Hamming distance.
This coherent "MEDseq" framework is developed with the aims of estimating the number of typical career trajectories, identifying the relevant features characterising the typical trajectories, and assessing the extent to which such patterns are shaped by the individuals' background characteristics. Simultaneously incorporating the sampling weights and the covariates in the clustering process allows new insights to be gleaned from the Northern Irish data.
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