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Our interest in the statistical modeling of data on the timing of recidivism began in the mid 1970s when we were both junior members of the eco nomics department at the University of North Carolina. At that time, methods of analyzing qualitative and limited variables were being developed rapidly in the econometric literature, and we became interested in finding a suitable application for these new methods. Data on the timing of recidivism offered unique and interesting statistical challenges, such as skewness of the distribution and the presence of censoring. Being young and foolish, we decided it would be fun to try something "really" difficult. And, being young and ignorant, we were blissfully unaware of the con current developments in the statistical modeling of survival times that were then appearing in the biostatistics, operations research, and criminological literatures. In the course of some earlier research, we had learned that the North Carolina Department of Correction had an unusually well-developed data base on their inmates. We approached the Department and asked if they would be interested in working with us to develop models that would predict when their former charges would return to their custody. They agreed because they were interested in using such models to evaluate rehabilitative programs and alternative prison management systems and to help project future prison populations.
Sommario
1 Introduction.- Overview.- Prediction in Criminology.- Ethical Issues.- What Sample Should Be Used to Estimate the Model?.- Selection of a Criterion Variable.- Use and Selection of Explanatory Variables.- Selection of a Statistical Model.- What Are Realistic Goals for Prediction?.- The Career Criminal Paradigm.- Previous Use of Survival Analysis in Justice Research.- Preview of Coming Attractions.- 2 Data.- The Nature of the Data.- Definitions of Variables.- Comparisons of Subsamples.- 3 Survey of Statistical Methodology.- Survival Time Models.- Estimation of Survival Time Models.- Predictions Using Survival Time Models.- 4 Simple Models.- Nonparametric Prediction.- The Exponential Distribution.- The Lognormal Model.- The Log-Logistic Model.- The Weibull Model.- The LaGuerre Model.- Conclusions.- 5 Split Population Models.- The Split Exponential Model.- The Split Lognormal Model.- The Split Log-Logistic Model.- The Split Weibull Model.- The Split LaGuerre Model.- Conclusions.- 6 The Proportional Hazards Model.- The Model and Its Estimation.- Results of Estimation.- Predictions From the Proportional Hazards Model.- Conclusions.- 7 Parametric Models With Explanatory Variables.- Models Based on the Exponential Distribution.- Results for Exponential Models.- Predictions From Exponential Models.- Models Based on the Lognormal Distribution.- Results for Lognormal Models.- Predictions From Lognormal Models.- A Model Based on the LaGuerre Distribution.- Conclusions.- 8 Predictions for Nonrandom Samples and for Individuals.- Predictions Across Release Cohorts.- Subsample Predictions.- Individual Predictions.- Conclusions.- 9 Summary and Conclusions.- Summary.- Conclusions.- References.- Author Index.
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Peter Schmidt, geboren 1944 in Gescher, Schriftsteller und Philosoph, gilt selbst dem Altmeister des Spionagethrillers, John le Carré, als einer der führenden deutschen Kriminalautoren des Genres. Außerdem veröffentlichte er bereits zahlreiche Medizinthriller, Wissenschaftsthriller, Psychothriller und Detektivromane. Im Jahr 1994 wurde er mit dem Literaturpreis "Ruhr" für das bisherige Gesamtwerk ausgezeichnet. Heute lebt Peter Schmidt in Gelsenkirchen.
