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.
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From February 2013 articles are under a Creative Commons license: CC BY-NC-ND You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work), you may not use the work for commercial purposes and you may not alter, transform, or build upon the work.
