| Autor: |
MUNIR HIABU, MARÍA DOLORES MARTÍNEZ-MIRANDA, JENS PERCH NIELSEN, JAAP SPREEUW, CARSTEN TANGGAARD, ANDRÉS M. VILLEGAS |
| Jazyk: |
angličtina |
| Předmět: |
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| Zdroj: |
Revista Colombiana de Estadística, Vol 38, Iss 2, Pp 399-411 |
| Druh dokumentu: |
article |
| ISSN: |
0120-1751 |
| DOI: |
10.15446/rce.v38n2.51668 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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