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A latent Markov model for detecting patterns of criminal activity.

Francis, Brian J. and Bartolucci, Francesco and Pennoni, Fulvia (2007) A latent Markov model for detecting patterns of criminal activity. Journal of the Royal Statistical Society Series A (Statistics in Society), 170 (1). pp. 115-132. ISSN 1467-985X

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Abstract

The paper investigates the problem of determining patterns of criminal behaviour from official criminal histories, concentrating on the variety and type of offending convictions. The analysis is carried out on the basis of a multivariate latent Markov model which allows for discrete covariates affecting the initial and the transition probabilities of the latent process. We also show some simplifications which reduce the number of parameters substantially; we include a Rasch-like parameterization of the conditional distribution of the response variables given the latent process and a constraint of partial homogeneity of the latent Markov chain. For the maximum likelihood estimation of the model we outline an EM algorithm based on recursions known in the hidden Markov literature, which make the estimation feasible also when the number of time occasions is large. Through this model, we analyse the conviction histories of a cohort of offenders who were born in England and Wales in 1953. The final model identifies five latent classes and specifies common transition probabilities for males and females between 5-year age periods, but with different initial probabilities.

Item Type: Article
Journal or Publication Title: Journal of the Royal Statistical Society Series A (Statistics in Society)
Additional Information: RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 2421
Deposited By: ep_importer
Deposited On: 31 Mar 2008 12:02
Refereed?: Yes
Published?: Published
Last Modified: 09 Oct 2013 15:17
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/2421

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