Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16117
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dc.contributor.authorJASPERS, Stijn-
dc.contributor.authorAERTS, Marc-
dc.contributor.authorVERBEKE, Geert-
dc.date.accessioned2014-01-10T09:41:54Z-
dc.date.available2014-01-10T09:41:54Z-
dc.date.issued2013-
dc.identifier.citationMuggeo,V.M.R.; Capursi, V.; Boscaino, G.; Lovison G. (Ed.). Proceedings of the 28th International Workshop on Statistical Modelling, Volume.1, p. 191-196-
dc.identifier.isbn978-88-96251-47-8-
dc.identifier.urihttp://hdl.handle.net/1942/16117-
dc.description.abstractAntimicrobial resistance has become one of the main public health burdens of the last decades, and monitoring the development and spread of non-wild-type isolates has therefore gained increased interest. Monitoring is performed, based on the Minimum Inhibition Concentration (MIC) values, which are collected through the application of dilution experiments. A semi-parametric mixture model is presented, which is able to estimate the full continuous MIC distribution. The model is based on an extended and censored-adjusted version of the penalized mixture approach often used in density estimation. A data application and simulation study are presented in which the promising behaviour of the new method is illustrated.-
dc.language.isoen-
dc.publisherIstituto Poligrafico Europeo-
dc.subject.otherAntimicrobial resistance; Censoring; Penalized mixture approach; Semi-parametric-
dc.titleEstimation of an MIC distribution using a two-stage semi-parametric mixture model-
dc.typeProceedings Paper-
local.bibliographicCitation.authorsMuggeo,V.M.R.-
local.bibliographicCitation.authorsCapursi, V.-
local.bibliographicCitation.authorsBoscaino, G.-
local.bibliographicCitation.authorsLovison G.-
local.bibliographicCitation.conferencedateJuly 8-12 2013-
local.bibliographicCitation.conferencename28th International Workshop on Statistical Modelling-
local.bibliographicCitation.conferenceplacePalermo-
dc.identifier.epage196-
dc.identifier.spage191-
local.bibliographicCitation.jcatC1-
local.publisher.placePalermo-
dc.relation.referencesEilers, P. and Marx, B. (1996). Flexible smoothing with b-splines and penalties. Statistical Science, 11, 89 - 121. Schellhase, C. and Kauermann, G. (2012). Density estimation and comparison with a penalized mixture approach. Computational Statistics, 27, 757 - 777.-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
local.bibliographicCitation.btitleProceedings of the 28th International Workshop on Statistical Modelling, Volume.1-
item.contributorVERBEKE, Geert-
item.contributorJASPERS, Stijn-
item.contributorAERTS, Marc-
item.accessRightsOpen Access-
item.fullcitationJASPERS, Stijn; AERTS, Marc & VERBEKE, Geert (2013) Estimation of an MIC distribution using a two-stage semi-parametric mixture model. In: Muggeo,V.M.R.; Capursi, V.; Boscaino, G.; Lovison G. (Ed.). Proceedings of the 28th International Workshop on Statistical Modelling, Volume.1, p. 191-196.-
item.fulltextWith Fulltext-
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