| Testing goodness of fit of parametric models for censored data. | |
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MedLine Citation:
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PMID: 22714389 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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 © 2012 John Wiley & Sons, Ltd. |
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Authors:
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Ruth Nysen; Marc Aerts; Christel Faes |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2012-6-19 |
Journal Detail:
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Title: Statistics in medicine Volume: - ISSN: 1097-0258 ISO Abbreviation: - Publication Date: 2012 Jun |
Date Detail:
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Created Date: 2012-6-20 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8215016 Medline TA: Stat Med Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2012 John Wiley & Sons, Ltd. |
Affiliation:
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Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Center for Statistics, Universiteit Hasselt, Agoralaan 1, B-3590 Diepenbeek, Belgium. ruth.nysen@uhasselt.be. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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