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Modelling Crime Rate Using a Mixed Effects Regression Model

Received: 2 October 2015     Accepted: 15 October 2015     Published: 28 October 2015
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Abstract

In this paper we propose a type of Mixed effects Regression Model, that is Hierarchical Linear Model to study crime rate. We derive the estimators of the proposed model and discuss the asymptotic properties of the model. In order to test for the practicability of the proposed model, we estimate a crime equation using a panel dataset of the provinces in Kenya for the period 1992 to 2012 employing the REML estimator. Our empirical results suggest that Poverty Rate, Unemployment rate, Probability of arrest, population Density and police rate are correlated to all typologies of crime rate considered. The results further suggest that crime rate is better explained at provincial level as compared to country level.

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6)
DOI 10.11648/j.ajtas.20150406.20
Page(s) 496-503
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Mixed Effects Model, Panel Data, Crime Rate, Kenya, Provinces

References
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[12] Marselli and Vannini. (1997). Estimating a Crime Equation in the Presence of Organized Crime:Evidence from Italy. International Review of Law and Economics 17, 89-113.
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  • APA Style

    Chris Muchwanju, Joel Cheruyiot Chelule, Joseph Mung’atu. (2015). Modelling Crime Rate Using a Mixed Effects Regression Model. American Journal of Theoretical and Applied Statistics, 4(6), 496-503. https://doi.org/10.11648/j.ajtas.20150406.20

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    ACS Style

    Chris Muchwanju; Joel Cheruyiot Chelule; Joseph Mung’atu. Modelling Crime Rate Using a Mixed Effects Regression Model. Am. J. Theor. Appl. Stat. 2015, 4(6), 496-503. doi: 10.11648/j.ajtas.20150406.20

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    AMA Style

    Chris Muchwanju, Joel Cheruyiot Chelule, Joseph Mung’atu. Modelling Crime Rate Using a Mixed Effects Regression Model. Am J Theor Appl Stat. 2015;4(6):496-503. doi: 10.11648/j.ajtas.20150406.20

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  • @article{10.11648/j.ajtas.20150406.20,
      author = {Chris Muchwanju and Joel Cheruyiot Chelule and Joseph Mung’atu},
      title = {Modelling Crime Rate Using a Mixed Effects Regression Model},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {4},
      number = {6},
      pages = {496-503},
      doi = {10.11648/j.ajtas.20150406.20},
      url = {https://doi.org/10.11648/j.ajtas.20150406.20},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150406.20},
      abstract = {In this paper we propose a type of Mixed effects Regression Model, that is Hierarchical Linear Model to study crime rate. We derive the estimators of the proposed model and discuss the asymptotic properties of the model. In order to test for the practicability of the proposed model, we estimate a crime equation using a panel dataset of the provinces in Kenya for the period 1992 to 2012 employing the REML estimator. Our empirical results suggest that Poverty Rate, Unemployment rate, Probability of arrest, population Density and police rate are correlated to all typologies of crime rate considered. The results further suggest that crime rate is better explained at provincial level as compared to country level.},
     year = {2015}
    }
    

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    T1  - Modelling Crime Rate Using a Mixed Effects Regression Model
    AU  - Chris Muchwanju
    AU  - Joel Cheruyiot Chelule
    AU  - Joseph Mung’atu
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    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
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    UR  - https://doi.org/10.11648/j.ajtas.20150406.20
    AB  - In this paper we propose a type of Mixed effects Regression Model, that is Hierarchical Linear Model to study crime rate. We derive the estimators of the proposed model and discuss the asymptotic properties of the model. In order to test for the practicability of the proposed model, we estimate a crime equation using a panel dataset of the provinces in Kenya for the period 1992 to 2012 employing the REML estimator. Our empirical results suggest that Poverty Rate, Unemployment rate, Probability of arrest, population Density and police rate are correlated to all typologies of crime rate considered. The results further suggest that crime rate is better explained at provincial level as compared to country level.
    VL  - 4
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Author Information
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

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