Resumen

HIABU, MUNIR et al. Global Polynomial Kernel Hazard Estimation. Rev.Colomb.Estad. [online]. 2015, vol.38, n.2, pp.399-411. ISSN 0120-1751.  https://doi.org/10.15446/rce.v38n2.51668.

This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically reduces bias with unchanged variance. A simulation study investigates the finite-sample properties of GPA. The method is tested on local constant and local linear estimators. From the simulation experiment we conclude that the global estimator improves the goodness-of-fit. An especially encouraging result is that the bias-correction works well for small samples, where traditional bias reduction methods have a tendency to fail.

Palabras clave : Kernel Estimation; HazardFunction; Local Linear Estimation; Boundary Kernels; Polynomial Correction.

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