Global Polynomial Kernel Hazard 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.

• Autores: Hiabu, Munir; Martínez-Miranda, María Dolores; Nielsen, Jens Perch; Spreeuw, Jaap; Tanggaard, Carsten; Villegas, Andrés M.
• Tipo de recurso: Article of journal
• Fecha de publicación: 2015
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: spa
• OAI Identifier: oai:repositorio.unal.edu.co:unal/66533
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/66533; http://bdigital.unal.edu.co/67561/
• Palabra clave: 51 Matemáticas / Mathematics; 31 Colecciones de estadística general / Statistics; Kernel Estimation; Hazard Function; Local Linear Estimation; Boundary Kernels; Polynomial Correction; Estimación kernel; Funciones de riesgo; Estimación local lineal; Kernels de frontera; Corrección polinomial.
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional