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Goodness-of-fit methods for probabilistic index models

Jan De Neve (UGent) , Olivier Thas (UGent) and Jean-Pierre Ottoy (UGent)
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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.
Keywords
Goodness-of-fit, REGRESSION, Lack-of-fit, Linear smoothers, Probabilistic index models

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Citation

Please use this url to cite or link to this publication:

Chicago
De Neve, Jan, Olivier Thas, and Jean-Pierre Ottoy. 2013. “Goodness-of-fit Methods for Probabilistic Index Models.” Communications in Statistics-theory and Methods 42 (7): 1193–1207.
APA
De Neve, Jan, Thas, O., & Ottoy, J.-P. (2013). Goodness-of-fit methods for probabilistic index models. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 42(7), 1193–1207.
Vancouver
1.
De Neve J, Thas O, Ottoy J-P. Goodness-of-fit methods for probabilistic index models. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS. 2013;42(7):1193–207.
MLA
De Neve, Jan, Olivier Thas, and Jean-Pierre Ottoy. “Goodness-of-fit Methods for Probabilistic Index Models.” COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 42.7 (2013): 1193–1207. Print.
@article{2109261,
  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.},
  author       = {De Neve, Jan and Thas, Olivier and Ottoy, Jean-Pierre},
  issn         = {0361-0926},
  journal      = {COMMUNICATIONS IN STATISTICS-THEORY AND METHODS},
  keyword      = {Goodness-of-fit,REGRESSION,Lack-of-fit,Linear smoothers,Probabilistic index models},
  language     = {eng},
  number       = {7},
  pages        = {1193--1207},
  title        = {Goodness-of-fit methods for probabilistic index models},
  url          = {http://dx.doi.org/10.1080/03610926.2012.695851},
  volume       = {42},
  year         = {2013},
}

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