Seminar Series
Fall 1997


 
Date
October 3, 1997
Time
11:00 am
Location
Room 102D, Cullen College of Engineering
Topic
Estimation of Arrest Careers Using Hierarchical Stochastic Models
Speaker
Dr. Chul Woo Ahn 

Dr. Chul is an Associate professor at the University of Texas Health Science Center.  He received his Ph.D. from Carnegie-Mellon University in 1986.  He served as a reviewer for NSF, NIH, NIJ grant applications.  He also served as a reviewer in Epidemiology and Disease Control (EDC-1) study section.  Currently, he is serving as a principal investigator or co-investigator on the following grant projects:

    (1) General Clinical Research Center
    (2) Specialized Center for Research in Scleroderma
    (3) Supplementary Grant for Clinical Psychopharmacology Computer Laboratory
He has submitted the following grant proposals as a principal investigator for funding:
    (1) Estimation of Criminal Career Parameters using Stochastic Models
    (2) Elder abuse and ethnicity in Texas
His current interest is on the design and analysis of clinical trials, stochastic modeling, and correlated categorical data.  He has more than 100 Peer-reviewed publications. 
Abstract
This talk introduces a general procedure using hierarchical stochastic models for characterizing criminal careers within a population of heterogeneous offenders. Individuals engage in criminal careers that are treated as stochastic processes governed by fixed parameters, and these parameters come from specified distributions. The parameters of these distributions at the upper level of hierarchy must then be specified.  The models are estimated using data on all persons arrested at least once in the six-county Detroit Standard Metropolitan Statistical Area during the 4 years 1974-1977 for a criterion offense (an index crime other than larceny) and arrested at least once for robbery through April 1979. The collected data set is not a random sample of all such offenders in the population. There is a bias toward selecting those with a higher arrest frequency. In order to make more general inferences, statistical adjustment was needed to overcome the arrest-frequency sampling bias. We consider a series of models for the arrest career and fit the models  with the data set of arrests. After correcting biases in the data set, we estimate the model parameters using  Gibbs sampling methods and then examine the resulting models.
Note
All Are Welcome.  Refreshments will be served.