Influence diagnostics in exponentiated-Weibull regression models with censored data.
Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from the error assumptions and the presence of outliers and influential observations with the fitted models. The literature provides plenty of approaches for detecting outlying or influentia...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2006 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3793 |
| Acceso en línea: | https://hdl.handle.net/2099/3793 |
| Access Level: | acceso abierto |
| Palabra clave: | Multivariate analysis Inference Survival Analysis Anàlisi multivariable Inferència Estadística Classificació AMS::62 Statistics::62H Multivariate analysis Classificació AMS::62 Statistics::62J Linear inference, regression Classificació AMS::62 Statistics::62N Survival analysis and censored data |
| Sumario: | Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from the error assumptions and the presence of outliers and influential observations with the fitted models. The literature provides plenty of approaches for detecting outlying or influential observations in data sets. In this paper, we follow the local influence approach (Cook 1986) in detecting influential observations with exponentiated-Weibull regression models. The relevance of the approach is illustrated with a real data set, where it is shown that by removing the most influential observations, there is a change in the decision about which model fits the data better. |
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