¹City University London, Cass Business School, Faculty of Actuarial Science and Insurance, United Kingdom. Ph.D. Student. Email: Munir.Hiabu.1@cass.city.ac.uk
²City University London, Cass Business School, Faculty of Actuarial Science and Insurance, United Kingdom. University of Granada, Faculty of Sciences, Department of Statistics and O.R., Spain. Associate Professor. Email: mmiranda@ugr.es
³City University London, Cass Business School, Faculty of Actuarial Science and Insurance, United Kingdom. Professor. Email: Jens.Nielsen.1@city.ac.uk
⁴City University London, Cass Business School, Faculty of Actuarial Science and Insurance, United Kingdom. Senior Lecturer. Email: J.Spreeuw@city.ac.uk
⁵Aarhus University, CREATES, Denmark. Professor. Email: ctanggaard@creates.au.dk
⁶City University London, Cass Business School, Faculty of Actuarial Science and Insurance, United Kingdom. Ph.D. Student. Email: Andres.Villegas.1@cass.city.ac.uk

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.

Key words: Kernel Estimation, HazardFunction, Local Linear Estimation, Boundary Kernels, Polynomial Correction.

En este artículo se introduce un nuevo método de correción del sesgo para la estimación núcleo de la función de riesgo. El método, denominado ajuste polinomial global (APG), consiste en una corrección global que es aplicable a cualquier tipo de estimador núcleo de la función de riesgo. Se comprueba que APG posee buenas propiedades asintóticas y que consigue reducir el sesgo sin incrementar la varianza. Se realizan estudios de simulación para evaluar las propiedades del APG en muestras finitas. Dichos estudios muestran un buen comportamiento en la práctica del APG. Esto es especialmente alentador dado que para muestras finitas los métodos tradicionales de reducción del sesgo tienden a tener un comportamiento bastante pobre.

Palabras clave: estimación kernel, funciones de riesgo, estimación local lineal, kernels de frontera, corrección polinomial.

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