Computation of Nonparametric Convex Hazard Estimators via Profile Methods Technical Report 542 Department of Statistics, University of Washington
نویسندگان
چکیده
Abstract. In this paper we develop an algorithm to find the maximum likelihood estimator of a convex hazard function. The maximization is done in two steps. First, we use the support reduction algorithm of [GJW1] to find the profile likelihood over a constrained space. We next show that (−1) times the profile likelihood is bathtub-shaped in the parameters, and use a bisection algorithm to find the overall maximizer. We use the same approach to find a least squares estimator of a convex hazard rate. Simulations and data examples are also given.
منابع مشابه
Computation of nonparametric convex hazard estimators via profile methods.
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 i...
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