Accommodating covariates roc analysis
For factors that aﬀect discrimination (i.e., the ROC curve), we describe methods for modeling the ROC curve as a function of covariates.This paper applies receiver operating characteristic (ROC) analysis to micro-level, monthly time series from the M3-Competition.Forecasts from competing methods were used in binary decision rules to forecast exceptionally large declines in demand.Data on socio-demographic characteristics, lifestyle and cognitive-related factors, and type, place and frequency of preferred gambling were obtained by a self-administered questionnaire.
The following variables associated with PG were identified: instant gratification games, alcohol abuse, cognitive distortion, illegal behaviours and having started gambling with a relative or a friend.
Journal - very nice intro, nonmath, but insightful what has to be done...., includes explanation on PARAMETRIC vs non PARAMETRIC ROC estimation (smooth vs empirical), cf concept of partial AUC ROC--- give nice and simple overview on assumptions, and when PARAMETRIC vs NONparametric methods should be used.
Accommodating covariates roc analysis comments