Global Polynomial Kernel Hazard Estimation
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional de Colombia
2015
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| _version_ | 1876470710789996544 |
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| author | Munir Hiabu |
| author_facet | Munir Hiabu |
| contents | Global Polynomial Kernel Hazard Estimation Munir Hiabu María Dolores Martínez-Miranda Jens Perch Nielsen Jaap Spreeuw Carsten Tanggaard Andrés M. Villegas Física, Astronomía y Matemáticas Hazard Function Boundary Kernels Kernel Estimation Polynomial Correction Local Linear Estimation 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. 2015 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89940050006 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.2 Vol.38 |
| format | Artículo científico |
| id | redalyc_89940050006 |
| institution | Redalyc |
| language | en |
| publishDate | 2015 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | Global Polynomial Kernel Hazard Estimation Munir Hiabu Física, Astronomía y Matemáticas Hazard Function Boundary Kernels Kernel Estimation Polynomial Correction Local Linear Estimation Global Polynomial Kernel Hazard Estimation Munir Hiabu María Dolores Martínez-Miranda Jens Perch Nielsen Jaap Spreeuw Carsten Tanggaard Andrés M. Villegas Física, Astronomía y Matemáticas Hazard Function Boundary Kernels Kernel Estimation Polynomial Correction Local Linear Estimation 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. 2015 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89940050006 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.2 Vol.38 |
| title | Global Polynomial Kernel Hazard Estimation |
| topic | Física, Astronomía y Matemáticas Hazard Function Boundary Kernels Kernel Estimation Polynomial Correction Local Linear Estimation |
| url | https://www.redalyc.org/articulo.oa?id=89940050006 |