Comparing models for sequence data: prediction and dissimilarities

Raffaella Piccarreta, Marco Bonetti and Stefano Lombardi

No 113, Working Papers from "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi

Abstract: We consider the case when it is of interest to study the different states experienced over time by a set of subjects, focusing on the resulting trajectories as a whole rather than on the occurrence of specific events. Such situation occurs commonly in a variety of settings, for example in social and biomedical studies. Model‐based approaches, such as multistate models or Hidden Markov models, are being used increasingly to analyze trajectories and to study their relationships with a set of explanatory variables. The different assumptions underlying different models typically make the comparison of their performances difficult. In this work we introduce a novel way to accomplish this task, based on microsimulation‐based predictions. We discuss some criteria to evaluate one model and/or to compare competing models with respect to their ability to generate trajectories similar to the observed ones.

Keywords: Dissimilarity; Hidden Markov model; Interpoint distance distribution; Micro‐simulation; Multistate model; Optimal Matching; Sequence analysis (search for similar items in EconPapers)
Pages: 31 pages
Date: 2018-01
New Economics Papers: this item is included in nep-ecm
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