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.

Bartolucci, F., Pennoni, F., Francis, B. (2007). A latent Markov model for detecting pattern of criminal activity. JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY, 170(1), 115-132 [10.1111/j.1467-985X.2006.00440.x].

A latent Markov model for detecting pattern of criminal activity

PENNONI, FULVIA;
2007

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.
Articolo in rivista - Articolo scientifico
Classification; Criminal trajectories; EM algorithm; Latent class model; Markov chains; Rasch model
English
2007
170
1
115
132
none
Bartolucci, F., Pennoni, F., Francis, B. (2007). A latent Markov model for detecting pattern of criminal activity. JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY, 170(1), 115-132 [10.1111/j.1467-985X.2006.00440.x].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/7173
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