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 |
Mixed Effects Model, Panel Data, Crime Rate, Kenya, Provinces
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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
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
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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Download@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}
}
TY - JOUR T1 - Modelling Crime Rate Using a Mixed Effects Regression Model AU - Chris Muchwanju AU - Joel Cheruyiot Chelule AU - Joseph Mung’atu Y1 - 2015/10/28 PY - 2015 N1 - https://doi.org/10.11648/j.ajtas.20150406.20 DO - 10.11648/j.ajtas.20150406.20 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 496 EP - 503 PB - Science Publishing Group SN - 2326-9006 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 IS - 6 ER -
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