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Goodness-of-fit methods for probabilistic index models

Jan De Neve UGent, Olivier Thas UGent and Jean-Pierre Ottoy UGent (2013) COMMUNICATIONS IN STATISTICS-THEORY AND METHODS. 42(7). p.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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Goodness-of-fit, REGRESSION, Lack-of-fit, Linear smoothers, Probabilistic index models
journal title
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Commun. Stat.-Theory Methods
volume
42
issue
7
issue title
European young statisticians
pages
1193 - 1207
Web of Science type
Article
Web of Science id
000321690400005
JCR category
STATISTICS & PROBABILITY
JCR impact factor
0.289 (2013)
JCR rank
117/119 (2013)
JCR quartile
4 (2013)
ISSN
0361-0926
DOI
10.1080/03610926.2012.695851
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2109261
handle
http://hdl.handle.net/1854/LU-2109261
date created
2012-05-20 13:14:19
date last changed
2015-04-03 10:10:20
@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},
}

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.