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A latent Markov model for detecting patterns of criminal activity.

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A latent Markov model for detecting patterns of criminal activity. / Francis, Brian J.; Bartolucci, Francesco; Pennoni, Fulvia.

In: Journal of the Royal Statistical Society: Series A (Statistics in Society), Vol. 170, No. 1, 01.2007, p. 115-132.

Research output: Contribution to journalJournal article

Harvard

Francis, BJ, Bartolucci, F & Pennoni, F 2007, 'A latent Markov model for detecting patterns of criminal activity.', Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 170, no. 1, pp. 115-132. https://doi.org/10.1111/j.1467-985X.2006.00440.x

APA

Francis, B. J., Bartolucci, F., & Pennoni, F. (2007). A latent Markov model for detecting patterns of criminal activity. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(1), 115-132. https://doi.org/10.1111/j.1467-985X.2006.00440.x

Vancouver

Francis BJ, Bartolucci F, Pennoni F. A latent Markov model for detecting patterns of criminal activity. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2007 Jan;170(1):115-132. https://doi.org/10.1111/j.1467-985X.2006.00440.x

Author

Francis, Brian J. ; Bartolucci, Francesco ; Pennoni, Fulvia. / A latent Markov model for detecting patterns of criminal activity. In: Journal of the Royal Statistical Society: Series A (Statistics in Society). 2007 ; Vol. 170, No. 1. pp. 115-132.

Bibtex

@article{2c17b70e6ef24d08828b3a589761a70d,
title = "A latent Markov model for detecting patterns of criminal activity.",
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.",
author = "Francis, {Brian J.} and Francesco Bartolucci and Fulvia Pennoni",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2007",
month = "1",
doi = "10.1111/j.1467-985X.2006.00440.x",
language = "English",
volume = "170",
pages = "115--132",
journal = "Journal of the Royal Statistical Society: Series A (Statistics in Society)",
issn = "0964-1998",
publisher = "Wiley",
number = "1",

}

RIS

TY - JOUR

T1 - A latent Markov model for detecting patterns of criminal activity.

AU - Francis, Brian J.

AU - Bartolucci, Francesco

AU - Pennoni, Fulvia

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2007/1

Y1 - 2007/1

N2 - 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.

AB - 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.

U2 - 10.1111/j.1467-985X.2006.00440.x

DO - 10.1111/j.1467-985X.2006.00440.x

M3 - Journal article

VL - 170

SP - 115

EP - 132

JO - Journal of the Royal Statistical Society: Series A (Statistics in Society)

JF - Journal of the Royal Statistical Society: Series A (Statistics in Society)

SN - 0964-1998

IS - 1

ER -