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A Bayesian Quantile Regression Analysis of Potential Risk Factors for Violent Crimes in USA

Author(s): Ming Wang | Lijun Zhang

Journal: Open Journal of Statistics
ISSN 2161-718X

Volume: 02;
Issue: 05;
Start page: 526;
Date: 2012;
Original page

Keywords: Bayesian Quantile Regression | Asymmetric Laplace Distribution | Improper Priors | Sensitivity | Ordinary Least Square

Bayesian quantile regression has drawn more attention in widespread applications recently. Yu and Moyeed (2001) proposed an asymmetric Laplace distribution to provide likelihood based mechanism for Bayesian inference of quantile regression models. In this work, the primary objective is to evaluate the performance of Bayesian quantile regression compared with simple regression and quantile regression through simulation and with application to a crime dataset from 50 USA states for assessing the effect of potential risk factors on the violent crime rate. This paper also explores improper priors, and conducts sensitivity analysis on the parameter estimates. The data analysis reveals that the percent of population that are single parents always has a significant positive influence on violent crimes occurrence, and Bayesian quantile regression provides more comprehensive statistical description of this association.
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