Department of Economics and Business Economics

Global Polynomial Kernel Hazard Estimation: Ajuste polinomial global para la estimación kernel de la función de riesgo

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Munir Hiabu, Cass Business School, United Kingdom
  • Maria Dolores Martínez Miranda, Cass Business School, University of Granada, Spain
  • Jens Perch Nielsen, Cass Business School, London, United Kingdom
  • Jaap Spreeuw, Cass Business School, United Kingdom
  • Carsten Tanggaard
  • Andrés Villegas, Cass Business School, United Kingdom
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.
Original languageEnglish
JournalRevista Colombiana de Estadistica
Volume38
Issue2
Pages (from-to)399-411
ISSN0120-1751
DOIs
Publication statusPublished - Jul 2015

    Research areas

  • Kernel estimation, Hazard function, Local linear estimation, Boundary kernels, Polynomial correction

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