Goodness-of-fit methods for probabilistic index models

Authors

Jan De Neve, Ghent University
Olivier Thas, University of WollongongFollow
Jean-Pierre Ottoy, Ghent University

RIS ID

78334

Publication Details

De Neve, J., Thas, O. & Ottoy, J. (2013). Goodness-of-fit methods for probabilistic index models. Communications in Statistics: Theory and Methods, 42 (7), 1193-1207.

Abstract

A class of semiparametric regression models, called probabilistic index models, has been recently proposed. Because these models are semiparametric, inference is only valid when the proposed model is consistent with the underlying data-generating model. However, no formal goodness-of-fit methods for these probabilistic index models exist yet. We propose a test and a graphical tool for assessing the model adequacy. Simulation results indicate that both methods succeed in detecting lack-of-fit. The methods are also illustrated on a case study.