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Assessing Influence in Regression Analysis with Censored Data

Luis A. Escobar and William Q. Meeker, Jr.
Biometrics
Vol. 48, No. 2 (Jun., 1992), pp. 507-528
DOI: 10.2307/2532306
Stable URL: http://www.jstor.org/stable/2532306
Page Count: 22
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Assessing Influence in Regression Analysis with Censored Data
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Abstract

In this paper we show how to evaluate the effect that perturbations to the model, data, or case weights have on maximum likelihood estimates from censored survival data. The ideas and methods also apply to other nonlinear estimation problems. We review the ideas behind using log-likelihood displacement and local influence methods. We describe new interpretations for some local influence statistics and show how these statistics extend and complement traditional case deletion influence statistics for linear least squares. These statistics identify individual and combinations of cases that have important influence on estimates of parameters and functions of these parameters. We illustrate the methods by reanalyzing the Stanford Heart Transplant data with a parametric regression model.

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