Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2006

Influence diagnostics in exponentiated-Weibull regression models with censored data

  • Identification data

    Identifier: RP:2303
    Handle: https://hdl.handle.net/20.500.11797/RP2303
    Authors:
    Bolfarine, HelenoCancho, Vicente G.Ortega, Edwin M. M.
    Abstract:
    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.

  • Others:

    URV's Author/s: Bolfarine, Heleno Cancho, Vicente G. Ortega, Edwin M. M.
    Abstract: 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.
    Journal publication year: 2006
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article