Modelling Crime Rate Using a Mixed Effects Regression Model
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 6, November 2015, Pages: 496-503
Received: Oct. 2, 2015; Accepted: Oct. 15, 2015; Published: Oct. 28, 2015
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Chris Muchwanju, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Joel Cheruyiot Chelule, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Joseph Mung’atu, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
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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.
Mixed Effects Model, Panel Data, Crime Rate, Kenya, Provinces
To cite this article
Chris Muchwanju, Joel Cheruyiot Chelule, Joseph Mung’atu, Modelling Crime Rate Using a Mixed Effects Regression Model, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 6, 2015, pp. 496-503. doi: 10.11648/j.ajtas.20150406.20
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