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Title: Model averaging quantiles for censored data
Authors: NYSEN, Ruth 
AERTS, Marc 
FAES, Christel 
Issue Date: 2013
Publisher: Istituto Poligrafico Europeo
Source: 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
Abstract: Quantiles 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.
Keywords: Censoring; Model averaging; Quantiles
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ISBN: 978-88-96251-47-8
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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