Quantile estimation of the rejection distribution of food products integrating assessor values and interval-censored consumer data
Fitting parametric survival models with interval-censored data is a common task in survival analysis and implemented in many statistical software packages. Here, we present a novel approach to fit such models if the values on the scale of interest are measured with error. Random effects ANOVA models...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:117891 |
| Acceso en línea: | https://ddd.uab.cat/record/117891 |
| Access Level: | acceso abierto |
| Palabra clave: | Interval-censored data Maximization of the likelihood function Parametric survival model Sensory shelf-life data |
| Sumario: | Fitting parametric survival models with interval-censored data is a common task in survival analysis and implemented in many statistical software packages. Here, we present a novel approach to fit such models if the values on the scale of interest 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 numerical methods. An illustration is provided with a real data set on the rejection of yogurt as a function of its acid taste. |
|---|