Forecasting Dangerous Inmate Misconduct: An Application of Ensemble Statistical Procedures

By Richard A. Berk, Brian Kriegler, Jong-Ho Baek. 2005

91 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cynthia Rudin
2. Berk Ustun
3. Jiaming Zeng
4. G. Barnes
5. Richard A. Berk
6. Justin Bleich
7. Susan B. Sorenson
8. E. Kurtz

In Sociology and Political Science

1. Richard A. Berk
2. Cynthia Rudin
3. G. Barnes
4. Berk Ustun
5. Jiaming Zeng
6. Justin Bleich
7. Susan B. Sorenson
8. E. Kurtz

In Statistics and Probability

1. Cynthia Rudin
2. Richard A. Berk
3. Berk Ustun
4. Jiaming Zeng
5. Justin Bleich
6. G. Barnes
7. E. Kurtz
8. Susan B. Sorenson

Citations in the corpus

• Interpretable classification models for recidivism prediction (2015, methodology): “, 2011); predicting serious misconduct among incarcerated prisoners (Berk et al., 2006); forecasting potential murders for criminals on probation or parole (Berk et al.”
• A REVIEW OF STATISTICAL METHODS FOR DETERMINATION OF RELATIVE IMPORTANCE OF CORRELATED PREDICTORS AND IDENTIFICATION OF DRIVERS OF CONSUMER LIKING (2012, background): “There are many applications of variable-importance measures yielded by RFs (cf., e.g., Lunetta et al. 2004; Bureau et al. 2005; Huang et al. 2005; Berk et al. 2006; Diaz-Uriarte and de Andres 2006; Qi et al. 2006).”
• Forecasts of Violence to Inform Sentencing Decisions (2013, background): “One might ordinarily apply a tree-based form of machine learning already shown to be effective in related circumstances (Barnes et al. 2010; Berk et al. 2005, 2006, 2009).”
• Small Area Estimation of the Homeless in Los Angeles: An Application of Cost-Sensitive stochastic Gradient Boosting (2010, background): “Compute the baseline relative influence as the average relative influence from steps (1)-(3).”
• Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions (2016, background): “…when they arrive at the scene (Berk et al. 2005), mis-conduct in prison to help prison administrators assign inmates to appropriate security levels (Berk et al. 2006), homicides committed by probationers or parolees to identify individuals who pose a very serious threat to public safety (Berk et…”
• Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning (2009, methodology): “We have had some success with this implementation of random forests in previous work (Berk et al., 2005, 2006; Lennert-Cody and Berk, 2007).”