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Regression diagnostic methods are extended to the exponential and Weibull distributions, yielding diagnostics comparable to Cook's distance and Belsley, Kuh, and Welsch's DFBETAS. These diagnostics are simple, cheap to compute, and effectively identify outlying cases in the response and factor variables. When applied to five well-analyzed datasets, these diagnostics show that outliers exist in survival analyses and can matter just as they do in ordinary regression. The same cases have almost identical effects on the parameters if the Cox model is used instead.

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