Comparing models for sequence data: prediction and dissimilarities

Number: 113
Year: 2018
Author(s): Raffaella Piccarreta, Marco Bonetti, Stefano Lombardi
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 ofspecific 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.

Raffaella Piccarreta Bocconi University, Italy

Marco Bonetti Bocconi University, Italy

Stefano Lombardi Department of Economics, Uppsala University, Sweden

Language: English

The paper may be downloaded here.

Keywords: Dissimilarity,Hidden Markov model,Interpoint distance distribution,Micro‐simulation,Multi‐state model,Optimal Matching,Sequence analysis