Inference for Parametric Regression Models

  • John P. Klein
  • Melvin L. Moeschberger
Part of the Statistics for Biology and Health book series (SBH)

Abstract

In previous chapters, we focused on nonparametric methods for describing the survival experience of a population and regression models for survival data which do not require any specific distributional assumptions about the shape of the survival function. In this chapter, we shall discuss the use of parametric models for estimating univariate survival and for the censored-data regression problem. When these parametric models provide a good fit to data, they tend to give more precise estimates of the quantities of interest because these estimates are based on fewer parameters. Of course, if the parametric model is chosen incorrectly, it may lead to consistent estimators of the wrong quantity.

Keywords

Acceleration Factor Weibull Model Deviance Residual Parametric Regression Model Weibull Regression Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • John P. Klein
    • 1
  • Melvin L. Moeschberger
    • 2
  1. 1.Division of BiostatisticsMedical College of WisconsinMilwaukeeUSA
  2. 2.School of Public Health, Division of Epidemiology and BiometricsThe Ohio State University Medical CenterColumbusUSA

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