|
Activity Number:
|
475
- Statistical Computing and Inference
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Statistical Computing
|
|
Abstract #322741
|
|
|
Title:
|
Studying Crime Trends in the United States in the XXI Century
|
|
Author(s):
|
Xuwen Zhu* and Volodymyr Melnykov
|
|
Companies:
|
The University of Louisville and The University of Alabama
|
|
Keywords:
|
crime trend ;
finite mixture models ;
model-based clustering ;
skewness ;
matrix observation
|
|
Abstract:
|
Studying crime trends and tendencies is an important problem that helps 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 is provided illustrating 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.
|
Authors who are presenting talks have a * after their name.
