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Nonparametric Estimation and Testing in Survival Models

In: Probability, Statistics and Modelling in Public Health

Author

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  • Henning Läuter

    (University of Potsdam, Institute of Mathematics)

  • Hannelore Liero

    (University of Potsdam, Institute of Mathematics)

Abstract

The aim of this paper is to demonstrate that nonparametric smoothing methods for estimating functions can be an useful tool in the analysis of life time data. After stating some basic notations we will present a data example. Applying standard parametric methods to these data we will see that this approach fails - basic features of the underlying functions are not reflected by their estimates. Our proposal is to use nonparametric estimation methods. These methods are explained in section 2. Nonparametric approaches are better in the sense that they are more flexible, and misspecifications of the model are avoided. But, parametric models have the advantage that the parameters can be interpreted. So, finally, we will formulate a test procedure to check whether a parametric or a nonparametric model is appropriate.

Suggested Citation

  • Henning Läuter & Hannelore Liero, 2006. "Nonparametric Estimation and Testing in Survival Models," Springer Books, in: Mikhail Nikulin & Daniel Commenges & Catherine Huber (ed.), Probability, Statistics and Modelling in Public Health, pages 319-331, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-26023-5_21
    DOI: 10.1007/0-387-26023-4_21
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