Studying crime trends in the USA over the years 2000–2012
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Studying crime trends and tendencies is an important problem that helps to identify socioeconomic patterns and relationships of crucial significance. Finite mixture models are famous for their flexibility in modeling heterogeneity in data. A novel approach designed for accounting for skewness in the distributions of matrix observations is proposed and applied to the United States crime data collected between 2000 and 2012 years. Then, the model is further extended by incorporating explanatory variables. A step-by-step model development demonstrates differences and improvements associated with every stage of the process. Results obtained by the final model are illustrated and thoroughly discussed. Multiple interesting conclusions have been drawn based on the developed model and obtained model-based clustering partition.
KeywordsCrime data Finite mixture model Matrix normal distribution Manly transformation EM algorithm
Mathematics Subject Classification62P25
The research is partially funded by the University of Louisville EVPRI internal research grant from the Office of the Executive Vice President for Research and Innovation.