Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14218
Title: Testing goodness of fit of parametric models for censored data
Authors: NYSEN, Ruth 
AERTS, Marc 
FAES, Christel 
Issue Date: 2012
Publisher: WILEY-BLACKWELL
Source: STATISTICS IN MEDICINE, 31 (21), p. 2374-2385
Abstract: We propose and study a goodness-of-fit test for left-censored, right-censored, and interval-censored data assuming random censorship. Main motivation comes from dietary exposure assessment in chemical risk assessment, where the determination of an appropriate distribution for concentration data is of major importance. We base the new goodness-of-fit test procedure proposed in this paper on the order selection test. As part of the testing procedure, we extend the null model to a series of nested alternative models for censored data. Then, we use a modified AIC model selection to select the best model to describe the data. If a model with one or more extra parameters is selected, then we reject the null hypothesis. As an alternative to the use of the asymptotic null distribution of the test statistic, we define a bootstrap-based procedure. We illustrate the applicability of the test procedure on data of cadmium concentrations and on data from the Signal Tandmobiel study and demonstrate its performance characteristics through simulation studies. Copyright (c) 2012 John Wiley & Sons, Ltd.
Notes: [Nysen, Ruth; Aerts, Marc; Faes, Christel] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat I BioSt, Ctr Stat, B-3590 Diepenbeek, Belgium. ruth.nysen@uhasselt.be
Keywords: Mathematical & Computational Biology; Public, Environmental & Occupational Health; Medical Informatics; Medicine, Research & Experimental; Statistics & Probability; bootstrap test; censored data; goodness-of-fit test; order selection test; semi-nonparametric estimator;bootstrap test; censored data; goodness-of-fit test; order selection test; semi-nonparametric estimator
Document URI: http://hdl.handle.net/1942/14218
ISSN: 0277-6715
e-ISSN: 1097-0258
DOI: 10.1002/sim.5368
ISI #: 000308209000007
Category: A1
Type: Journal Contribution
Validations: ecoom 2013
Appears in Collections:Research publications

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