Assessing the Predictive Utility of Logistic Regression, Classification and Regression Tree, Chi-Squared Automatic Interaction Detection, and Neural Network Models in Predicting Inmate Misconduct

Ngo, Fawn T.; Govindu, Ramakrishna; Agarwal, Anurag

doi:10.1007/s12103-014-9246-6

Assessing the Predictive Utility of Logistic Regression, Classification and Regression Tree, Chi-Squared Automatic Interaction Detection, and Neural Network Models in Predicting Inmate Misconduct

This study assesses the relative utility of a traditional regression approach - logistic regression (LR) - and three classification techniques - classification and regression tree (CART), chi-squared automatic interaction detection (CHAID), and multi-layer perceptron neural network (MLPNN)—in predic...

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VerfasserInnen:	Ngo, Fawn T. (VerfasserIn) ; Govindu, Ramakrishna (VerfasserIn) ; Agarwal, Anurag (VerfasserIn)
Medienart:	Elektronisch Aufsatz
Sprache:	Englisch
Veröffentlicht:	2015
In:	American journal of criminal justice Jahr: 2015, Band: 40, Heft: 1, Seiten: 47-74
Online-Zugang:	Volltext (lizenzpflichtig) Volltext (lizenzpflichtig)
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Schlagwörter:	Inmate misconduct Importation model Neural networks Chi-squared automatic interaction detection Classification and regression tree Logistic regression Comparative statistical techniques Actuarial risk assessment techniques

Assessing the predictive utility of logistic regression, classification and regression tree, chi-squared automatic interaction detection, and neural network models in predicting in...

von Ngo, Fawn T. (VerfasserIn) Agarwal, Anurag (VerfasserIn) Govindu, Ramakrishna (VerfasserIn)

in: American journal of criminal justice, 40 (2015), 1, Seite 47-74

Volltext (lizenzpflichtig)

Elektronisch Aufsatz