Latent Markov Modelling of Recidivism Data

Catrien C.J.H. Bijleveld; Ab Mooijaart

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Latent Markov Modelling of Recidivism Data

机译：累犯数据的隐马尔可夫模型

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This article discusses the application of latent Markov modelling for the analysis of recidivism data. We briefly examine the relations of Markov modelling with log–linear analysis, pointing out pertinent differences as well. We show how the restrictive Markov model may be more easily applicable by adding latent variables to the model, in which case the latent Markov model is a dynamic version of the latent class model. As an illustration, we apply latent Markov analysis on an empirical data set of juvenile prosecution careers, showing how the Markov analyses producing well-fitting and interpretable solutions. We end by comparing the possible contributions of Markov modelling in recidivism research, outlining its drawbacks as well. Recommendations and directions for future research conclude the article.

机译：本文讨论了隐马尔可夫模型在累犯数据分析中的应用。我们用对数线性分析简要地考察了马尔可夫建模的关系，并指出了相关的差异。我们展示了如何通过向模型添加潜在变量来更容易地应用限制性马尔可夫模型，在这种情况下，潜在马尔可夫模型是潜在类模型的动态版本。作为说明，我们将潜马尔可夫分析应用于未成年人起诉职业的经验数据集，以显示马尔可夫分析如何产生合适且可解释的解决方案。最后，我们比较了马尔可夫模型在累犯研究中的可能贡献，并概述了其缺点。总结本文的建议和未来研究的方向。

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来源
《Statistica neerlandica》 |2003年第3期|305-320|共16页
作者
Catrien C.J.H. Bijleveld; Ab Mooijaart;
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作者单位

NSCR Institute for the Study of Crime Law Enforcement Leiden P. O. Box 792 2300 AT Leiden the Netherlands;

Dept. Criminal Law and Criminology Free University Amsterdam The Netherlands;

Unit of Psychometrics Department of Psychology Leiden University P. O. Box 9555 2300 RB Leiden The Netherlands;

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原文格式 PDF
正文语种 eng
中图分类
关键词
recidivism; latent Markov analysis; juvenile delinquency;

机译：累犯;隐马尔可夫分析;少年犯罪;

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Latent Markov Modelling of Recidivism Data

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