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Global Polynomial Kernel Hazard Estimation

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

Publication Type: Article
Publisher Keywords: Kernel estimation; hazard function; local linear estimation; boundary kernels; polynomial correction.
Subjects: H Social Sciences > HG Finance
Departments: Bayes Business School > Actuarial Science & Insurance
Date available in CRO: 02 Mar 2015 17:04
Date deposited: 28 July 2017
Date of first online publication: December 2015
Text - Accepted Version
Available under License Creative Commons: Attribution 3.0.

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