Global Polynomial Kernel Hazard Estimation

Hiabu, M., Miranda, M. D. M., Nielsen, J. P., Spreeuw, J., Tanggaard, C. & Villegas, A. (2015). Global Polynomial Kernel Hazard Estimation. Revista Colombiana de Estadística, 38(2), pp. 399-411. doi: 10.15446/rce.v38n2.51668

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Kernel estimation; hazard function; local linear estimation; boundary kernels; polynomial correction.
Subjects: H Social Sciences > HG Finance
Divisions: Cass Business School > Faculty of Actuarial Science & Insurance
URI: http://openaccess.city.ac.uk/id/eprint/6732

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