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Computation of nonparametric convex hazard estimators via profile methods

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  • Hanna Jankowski
  • Jon Wellner

Abstract

This paper proposes a profile likelihood algorithm to compute the nonparametric maximum likelihood estimator of a convex hazard function. The maximisation is performed in two steps: First the support reduction algorithm is used to maximise the likelihood over all hazard functions with a given point of minimum (or antimode). Then it is shown that the profile (or partially maximised) likelihood is quasi-concave as a function of the antimode, so that a bisection algorithm can be applied to find the maximum of the profile likelihood, and hence also the global maximum. The new algorithm is illustrated using both artificial and real data, including lifetime data for Canadian males and females.

Suggested Citation

  • Hanna Jankowski & Jon Wellner, 2009. "Computation of nonparametric convex hazard estimators via profile methods," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(4), pages 505-518.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:4:p:505-518
    DOI: 10.1080/10485250902745359
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    Cited by:

    1. Quessy, Jean-François & Éthier, François, 2012. "Cramér–von Mises and characteristic function tests for the two and k-sample problems with dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2097-2111.
    2. Mary Meyer & Desale Habtzghi, 2011. "Nonparametric estimation of density and hazard rate functions with shape restrictions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 455-470.

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