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.
CITATION STYLE
Hiabu, M., Martínez-Miranda, M. D., Nielsen, J. P., Spreeuw, J., Tanggaard, C., & Villegas, A. M. (2015). Global polynomial kernel hazard estimation. Revista Colombiana de Estadistica, 38(2), 399–411. https://doi.org/10.15446/rce.v38n2.51668
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