Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16123
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dc.contributor.authorNYSEN, Ruth-
dc.contributor.authorAERTS, Marc-
dc.contributor.authorFAES, Christel-
dc.date.accessioned2014-01-10T10:32:40Z-
dc.date.available2014-01-10T10:32:40Z-
dc.date.issued2013-
dc.identifier.citationMuggeo, Vito M.R.; Capursi, Vincenza; Boscaino, Giovanni; Lovison, Gianfranco (Ed.). Proceedings of the 28th International Workshop on Statistical Modelling Volume 1, p. 307-312-
dc.identifier.isbn978-88-96251-47-8-
dc.identifier.urihttp://hdl.handle.net/1942/16123-
dc.description.abstractQuantiles are of interest in food safety data dealing with a limit of detection. The limit of detection introduces a lot of uncertainty in the left tail of the underlying distribution, making quantile estimation for this part of the distribution difficult. Therefore we fit a model to the data and derive the model-based estimate for the quantile. Since the true distribution is unknown, model averaging is used to combine information from a set of models. In this paper we discuss two approaches to use model averaging for quantiles. The methods are applied to a data example and compared in a simulation study. The effect of an increasing percentage of censoring on the estimates is explored.-
dc.language.isoen-
dc.publisherIstituto Poligrafico Europeo-
dc.subject.otherCensoring; Model averaging; Quantiles-
dc.titleModel averaging quantiles for censored data-
dc.typeProceedings Paper-
local.bibliographicCitation.authorsMuggeo, Vito M.R.-
local.bibliographicCitation.authorsCapursi, Vincenza-
local.bibliographicCitation.authorsBoscaino, Giovanni-
local.bibliographicCitation.authorsLovison, Gianfranco-
local.bibliographicCitation.conferencedate8 July 2013 - 12 July 2013-
local.bibliographicCitation.conferencename28th International Workshop on Statistical Modelling-
local.bibliographicCitation.conferenceplacePalermo, Italy-
dc.identifier.epage312-
dc.identifier.spage307-
local.bibliographicCitation.jcatC1-
local.publisher.placePalermo, Italy-
dc.relation.referencesBurnham, K.P. and Anderson, R.A. (1998). Model selection and inference: A practical information-theoretic approach. New York: Springer-Verlag. Fenton, V.M. and Gallant, A.R. (1996). Qualitative and asymptotic performance of SNP density estimators. Journal of Econometrics, 74, 77 - 118. Gallant, A.R. and Nychka, D.W. (1987) Semi-nonparametric maximum likelihood estimation. Econometrica, 55(2), 363 - 390. Nysen, R., Aerts, M. and Faes, C. (2012), Testing goodness of fit of parametric models for censored data. Statistics in Medicine, 31, 2374 - 2385.-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
local.bibliographicCitation.btitleProceedings of the 28th International Workshop on Statistical Modelling Volume 1-
item.fulltextWith Fulltext-
item.contributorNYSEN, Ruth-
item.contributorAERTS, Marc-
item.contributorFAES, Christel-
item.fullcitationNYSEN, Ruth; AERTS, Marc & FAES, Christel (2013) Model averaging quantiles for censored data. In: Muggeo, Vito M.R.; Capursi, Vincenza; Boscaino, Giovanni; Lovison, Gianfranco (Ed.). Proceedings of the 28th International Workshop on Statistical Modelling Volume 1, p. 307-312.-
item.accessRightsOpen Access-
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