Quantile estimation of the rejection distribution of food products integrating assessor values and interval-censored consumer data

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dc.contributor	Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.contributor	Universitat Politècnica de Catalunya. GREMA - Grup de Recerca en Estadística Matemàtica i les seves Aplicacions
dc.contributor.author	Langohr, Klaus
dc.contributor.author	Gómez Melis, Guadalupe
dc.contributor.author	Hough, Guillermo
dc.date	2013-12
dc.identifier.citation	Langohr, K.; Gomez, G.; Hough, G. Quantile estimation of the rejection distribution of food products integrating assessor values and interval-censored consumer data. "SORT: statistics and operations research transactions", Desembre 2013, vol. 37, núm. 2, p. 175-188.
dc.identifier.citation	1696-2281
dc.identifier.uri	http://hdl.handle.net/2117/21334
dc.language.iso	eng
dc.relation	http://www.idescat.cat/sort/sort372/37.2.4.langohr-etal.pdf
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Inferència estadística
dc.subject	Survival analysis (Biometry)
dc.subject	Mathematical statistics
dc.subject	Interval-censored data
dc.subject	maximization of the likelihood function
dc.subject	parametric survival model
dc.subject	sensory shelf-life data.
dc.subject	Estadística
dc.subject	62N Survival analysis and censored data
dc.subject	62F Parametric inference
dc.title	Quantile estimation of the rejection distribution of food products integrating assessor values and interval-censored consumer data
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/article
dc.description.abstract	Fitting parametric survival models with interval-censore d data is a common task in survival analysis and implemented in many statistical software pack ages. Here, we present a novel approach to fit such models if the values on the scale of intere st are measured with error. Random effects ANOVA models are used to account for the measurement errors and the likelihood function of the parametric survival model is maximized with numerica l methods. An illustration is provided with a real data set on the rejection of yogurt as a function of its acid taste.
dc.description.abstract	Peer Reviewed

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