Abdi, H., Valendin, D., & Edelman, B. (1999). Neural networks (Vol. 124). Newbury Park: Sage.
Aegisdottir, S., White, M. J., Spengler, P. M., Maugherman, A. S., Anderson, L. A., Cook, R. S., et al. (2006). The meta-analysis of clinical judgment project: fifty-six years of accumulated research on clinical versus statistical prediction. The Counseling Psychologist, 34
Aldrich, J. H., & Nelson, F. D. (1984). Linear probability, logit, and probit models (Vol. 45). Newbury Park: Sage.
Berk, R. A., & Bleich, J. (2013). Statistical procedures for forecasting criminal behavior: a comparative assessment. Criminology and Public Policy, 12
Bigi, R., Gregori, D., Cortigiani, L., Desideri, A., Chiarotto, F. A., & Toffolo, G. M. (2005). Artificial neural networks and robust Bayesian classifiers for risk stratification following uncomplicated myocardial infartion. International Journal of Cardiology, 101
Bishop, C. (1995). Neural networks for pattern recognition. New York: Oxford University Press.
Bonta, J. (1996). Risk-needs assessment and treatment. In A. T. Harland (Ed.), Choosing correctional options that work: defining the demand and evaluating the supply (pp. 18–22). Thousand Oaks: Sage.
Breiman, L. (2001). Decision tree forest. Machine Learning, 45
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey: Wadsworth and Brooks/Cole.
Brodzinski, J. D., Crable, E. A., & Scherer, R. F. (1994). Using artificial intelligence to model juvenile recidivism patterns. Computers in Human Services, 10
Bushway, S. D. (2013). Is there any logic to using logit: finding the right tool for the increasingly important job of risk prediction. Criminology and Public Policy, 12
Byrne, J. M., & Hummer, D. (2007). Myths and realities of prison violence: a review of the evidence. Victims and Offenders An International Journal of Evidence-based Research Policy and Practice, 2, 77–90.
Cao, L., Zhao, J., & Van Dine, S. (1997). Prison disciplinary tickets: a test of the deprivation and importation models. Journal of Crime and Justice, 25
Carpenter, & Grossberg. (1991). Causal attributions in expert parole decisions. Journal of Personality and Social Psychology, 36, 1501–1511.
Caulkins, J., Cohen, J., Gorr, W., & Wei, J. (1996). Predicting criminal recidivism: a comparison of neural network models with statistical methods. Journal of Crime and Justice, 24
Clemmer, D. (1940). The prison community. Boston: Christopher.
Coid, J., Yang, M., & Ullrich, S., et al. (2007). Predicting and understanding risk of reoffending: The prisoner cohort study. Research Summary, Ministry of Justice 6.
Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 243
Dhami, M. K., Ayton, P., & Lowenstein, G. (2007). Adaption to imprisonment: indigenous or imported? Criminal Justice and Behavior, 34
DiIulio, J. J., Jr. (1987). Governing prisons: a comparative study of correctional management. New York: Free Press.
Egan, J. P. (1975). Signal detection theory and ROC analysis. New York: Academic.
Florio, T., Einfeld, S., & Levy, F. (1994). Neural network and psychiatry: candidate applications in clinical decision making. Australian and New Zealander Psychiatry, 28
Friedman, J. H. (1999). Stochastic gradient boosting. Stanford: Stanford University.
Gardner, M. J., & Altman, D. G. (1989). Estimating with confidence. In M. J. Gardner & D. G. Altman (Eds.), Statistics with confidence (pp. 6–19). London: British Medical Journal.
Gardner, W., Lidz, C. W., Mulvey, E. P., & Shaw, E. C. (1996). A comparison of actuarial methods for identifying repetitively violent patients with mental illnesses. Law and Human Behavior, 20
Gendreau, P., Goggin, C. E., & Law, M. A. (1997). Predicting prison misconducts. Criminal Justice and Behavior, 24
Gendreau, P., Goggin, C. E., & Smith, P. (2002). Is the PCL-R really the “unparelleled” measure of offender-risk? A lesson in knowledge accumulation. Criminal Justice and Behavior, 29
Glover, A., Nicholson, D., Hemmati, T., Bernfeld, G., & Quinsey, V. (2002). A comparison of predictors of general and violent recidivism among high risk federal offenders. Criminal Justice & Behavior, 29, 235–249.
Goodstein, L., & Wright, K. N. (1989). Inmate adjustment to prison. In L. Goodstein & D. L. MacKenzie (Eds.), The American prison: issues in research and policy
(pp. 229–251). NY: Plenum.CrossRef
Gottfredson, S. D., & Gottfredson, D. M. (1986). Accuracy of prediction models. In A. Blumstein, J. Cohen, J. Roth, & C. A. Visher (Eds.), Criminal careers and “Career Criminals” (pp. 212–290). Washington: National Academy of Sciences Press.
Gottfredson, S. D., & Moriarty, L. J. (2006). Statistical risk assessment: old problems and new applications. Crime and Delinquency, 52
Grann, M., & Langstrom, N. (2007). Actuarial assessment of violence risk: to weigh or not to weigh? Criminal Justice and Behavior, 34
Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithm) prediction procedures: the clinical-statistical controversy. Psychology, Public Policy, and Law, 2
Guerriere, M. R., & Detsky, A. S. (1991). Neural networks: what are they? Annals of Internal Medicine, 115
Gurney, K. (1997). An Introduction to neural networks
. New York: UCL Press.CrossRef
Hanson, R. K. (2005). Twenty years of progress in violence risk assessment. Journal of Interpersonal Violence, 20
Hanson, R.K. & Morton-Bourgon, K.E (2007) The accuracy of recidivism risk assessments for sexual offenders: A meta-analysis. Public Safety and Emergency Preparedness Canada.
Hanson, R. K., & Morton-Bourgon, K. E. (2009). The accuracy of recidivism risk assessments for sexual offenders: a meta-analysis of 118 prediction studies. Psychological Assessment, 21
Harer, M. D., & Steffensmeier, D. J. (1996). Race and prison violence. Criminology, 34
Hill, T., & Lewicki, P. (2006). Statistics, methods and application: a comprehensive reference for science, industry, and data mining. Tulsa: StatSoft, Inc.
Hilton, N. Z., Harris, G. T., & Rice, M. E. (2006). Sixty-six years of research on the clinical versus actuarial prediction of violence. The Counseling Psychologist, 34
Hosmer, D. W., & Lemeshow, S. (1989). Applied logistic regression. New York: Wiley.
Irwin, J. K. (1981). Sociological studies of the impact of long term confinement. In D. A. Ward & K. F. Schoen (Eds.), Confinement in maximum custody (pp. 33–68). Lexington: D.C. Health.
Irwin, J. K., & Cressey, D. (1962). Thieves, convicts, and the inmate culture. Social Problems, 10
Jiang, S., & Fisher-Giorlando, M. (2002). Inmate misconduct: a test of the deprivation, importation, and situational models. The Prison Journal, 82
Jones, P. R. (1996). Risk prediction in criminal justice. In A. T. Harland (Ed.), Choosing correctional options that work: defining the demand and evaluating the supply (pp. 33–68). Thousand Oaks: Sage.
Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29
Kroner, D. G., & Mills, J. F. (2001). The accuracy of five appraisal risk instruments in predicting institutional misconduct and new convictions. Criminal Justice and Behavior, 28
Liu, Y. Y., Yang, M., Ramsay, M., Li, X. S., & Coid, J. W. (2011). A comparison of logistic regression, classification and regression tree, and neural networks models in predicting violent re-offending. Journal of Quantitative Criminology, 27
Loh, W. Y., & Shih, Y. S. (1997). Split selection methods for classification trees. Statistica Sinica, 7, 815–840.
Menzies, R., Webster, S. D., McMain, S., Staley, S., & Scaglione, R. (1994). The dimensions of dangerousness revisited. Law and Human Behavior, 18
Minsky, M., & Papert, S. (1969). Perceptrons. Cambridge: MIT Press.
Mossman, D. (1994). Assessing prediction of violence: being accurate about accuracy. Journal of Consulting and Clinical Psychology, 62
Neuilly, M., Zgoba, K. M., Tita, G. E., & Lee, S. S. (2011). Predicting recidivism in homicide offenders using classification tree analysis. Homicide Studies, 15, 154–176.
Ning, G. M., Su, J., Li, Y. Q., Wang, X. Y., Li, C. H., & Yan, W. M. (2006). Artificial neural network base model for cardiovascular risk stratification in hypertension. Medical and Biological Engineering and Computing, 44
Palocsay, S. W., Wang, P., & Brookshire, R. G. (2000). Predicting criminal recidivism using neural networks. Socio-Economic Planning Sciences, 34
Paterline, B. A., & Petersen, D. M. (1999). Structural and social psychological determinants of prisonization. Journal of Crime and Justice, 27
Perlich, C., Provost, F., & Simonof, J. (2003). Tree induction vs. logistic regression: a learning curve analysis. Journal of Machine Learning Research, 4, 211–255.
Price, R. K., Spitznagel, E. L., Downey, T. J., Meyer, D. J., Risk, N. K., & El-Ghazzawy, O. G. (2000). Applying artificial neural network models to clinical decision making. Psychological Assessment, 12
Rice, M. E., & Harris, G. T. (1995). Violent recidivism: assessing predictive validity. Journal of Consulting and Clinical Psychology, 63
Ridgeway, G. (2013). Linking prediction and prevention. Criminology and Public Policy, 12
Ripley, B. D. (1996). Pattern recognition and neural networks
. Cambridge: Cambridge University Press.CrossRef
Rokach, L., & Maimon, O. (2008). Data mining with decision trees: theory and application. Hackensack: World Scientific Publishing.
Rosenfeld, B., & Lewis, C. (2005). Assessing violent risk in stalking cases: a regression tree approach. Law and Human Behavior, 29
Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing (Vol. 1). Cambridge: MIT Press.
Rumelhart, D. E., & McClelland, J. L. (1988). Parallel distributed processing (Vol. 1 and 2). Cambridge: MIT Press.
Seymour, J. (1977). Niches in prisons. In H. Toch (Ed.), Living in prison: the ecology of survival (pp. 18–22). New York: Free Press.
Silver, E., Smith, W. R., & Banks, S. (2000). Constructing actuarial devices for predicting recidivism: a comparison of methods. Criminal Justice and Behavior, 27
Singh, J. P., & Fazel, S. (2010). Forensic risk assessment: a metareview. Criminal Justice and Behavior, 37
Singh, J. P., Grann, M., & Fazel, S. (2011). A comparative study of violence risk assessment tools: a systematic review and metaregression analysis of 68 studies and 25, 980 participants. Clinical Psychology Review
Snyder, H. N. (2011). Patterns & Trends: Arrests in the United States, 1980–2009. Bureau of Justice Statistics: U.S. Department of Justice.
Sorensen, J., Wrinkle, R., & Gutierrez, A. (1998). Patterns of rule-violating behaviors and adjustment to incarceration among murderers. The Prison Journal, 78
Stalans, L. J., Yarnold, P. R., Seng, M., Olson, D. E., & Repp, M. (2004). Identifying three types of violent offenders and predicting violent recidivism while on probation: a classification tree analysis. Law and Human Behavior, 28
StatSoft Inc (2008) Data mining, predictive analytics, statistics, StatSoft electronic textbook. http://www.statsoft.com/textbook/
Steadman, H. J., Silver, E., Monahan, J., Appelbaum, P. S., Robbins, P. C., & Mulvey, E. P. (2000). A classification tree approach to the development of actuarial violence risk assessment tools. Law and Human Behavior, 24
Steinke, P. (1991). Using situational factors to predict types of prison violence. Journal of Offender Rehabilitation, 17
Sykes, G. M. (1958). The society of captives. Princeton: Princeton University Press.
Thomas, S., & Leese, M. (2003). A green-fingered approach can improve the clinical utility of violence risk assessment tools. Criminal Behavior and Mental Health, 13
Thomas, S., Leese, M., Walsh, E., McCrone, P., Moran, P., & Burns, T. (2005). A comparison of statistical methods in predicting violence in psychotic illness. Comprehensive Psychiatry, 46
Toch, H. (1977). Living in prison: the ecology of survival. New York: Free Press.
Toch, H., & Adams, K. (1986). Pathology and disruptiveness among prison inmates. Journal of Research in Crime and Delinquency, 23
Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 9
Wallace, J. M., & Bachman, J. G. (1991). Explaining racial/ethnic differences in adolescent drug use: the impact of background and lifestyles. Social Forces, 38, 333–354.
White, H. (1989). Some asymptotic results for learning in single hidden-layer feedforward network models. Journal of the American Statistical Society, 84
Wooldredge, J. D. (1991). Correlates of deviant behavior among inmates of U.S. correctional facilities. Journal of Crime and Justice, 14
Wright, K. N. (1991). A study of individual, environmental, and interactive effects in explaining adjustment to prison. Justice Quarterly, 8
Yan, L., Dodier, R., Mozer, M.C., & Wolniewicz, R (2003) Optimizing classifier performance via the Wilcoxon-Mann–Whitney statistics. Proceedings of the International Conference on Machine Learning.
Yang, M., Liu, Y.Y. & Coid, J.W (2010) Applying neural networks and classification tree models to the classification of serious offenders and the prediction of recidivism. Research summary, Ministry of Justice, UK, available online at www.justice.gov.uk/publications/research.htm