Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16117
Title: Estimation of an MIC distribution using a two-stage semi-parametric mixture model
Authors: JASPERS, Stijn 
AERTS, Marc 
VERBEKE, Geert 
Issue Date: 2013
Publisher: Istituto Poligrafico Europeo
Source: 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
Abstract: Antimicrobial 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.
Keywords: Antimicrobial resistance; Censoring; Penalized mixture approach; Semi-parametric
Document URI: http://hdl.handle.net/1942/16117
ISBN: 978-88-96251-47-8
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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