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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Detalles Bibliográficos
Autores principales:	Ngo, Fawn T. (Autor) ; Govindu, Ramakrishna (Autor) ; Agarwal, Anurag (Autor)
Tipo de documento:	Electrónico Artículo
Lenguaje:	Inglés
Publicado:	2015
En:	American journal of criminal justice Año: 2015, Volumen: 40, Número: 1, Páginas: 47-74
Acceso en línea:	Volltext (lizenzpflichtig) Volltext (lizenzpflichtig)
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Verificar disponibilidad:	HBZ Gateway
Palabras clave:	Inmate misconduct Importation model Neural networks Chi-squared automatic interaction detection Classification and regression tree Logistic regression Comparative statistical techniques Actuarial risk assessment techniques