Title: Robust estimation for ordinal regression
Authors: Croux, Christophe ×
Haesbroeck, Gentiane
Ruwet, Christel #
Issue Date: 2013
Publisher: Elsevier
Series Title: Journal of Statistical Planning and Inference vol:143 issue:9 pages:1486-1499
Abstract: Ordinal regression is used for modelling an ordinal response variable as a function of some
explanatory variables.The classical technique for estimating the unknown parameters of this model is Maximum Likelihood(ML). The lack of robustness of this estimator is
formally shown by deriving its breakdown point and its influence function. To robustify the procedure, a weighting step is added to the Maximum Likelihood estimator,yielding
an estimator with bounded influence function. We also show that the loss inefficiency due to the weighting step remains limited. A diagnostic plot based on the Weighted
Maximum Likelihood estimator allows to detect outliers of different types in a single plot.
ISSN: 0378-3758
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
RobustEstimationFor.pdf Published 449KbAdobe PDFView/Open Request a copy

These files are only available to some KU Leuven Association staff members


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science