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Vol. 15 No. 2
julio-diciembre de 2015

ARTICULOS DE INVESTIGACION

Comparison of nonparametric estimators versus parametric for reliability function

https://doi.org/10.19053/1900771X.4246

Published 2015-06-26

  • Javier Ramírez-Montoya

Javier Ramírez-Montoya
Universidad de Sucre,Departamento de Matemáticas y Física, Grupo Investigación en Estadística y Aplicaciones GINESAP.

Abstract

One of the main objectives of the area of realiability is to estimate the function of reliability, where traditionally are used non parametric estimators, being more efficient in sample of big sizes. In this work nonparametric estimators are compared to the reliability function through the mean square error using nonparametric estimators of Kaplan & Meier (1958), Nelson estimator (1969) and Bootstrap applied to Kaplan & Meier and Nelson. The comparison is made considering the parametric estimates, through simulation with different scenarios, times of interest, sizes sample and percentages of censorship, showing that the Bootstrap resampling normal type does not present the best results with Kaplan & Meier. Using Nelson, the 18% was more efficient.

Keywords

bootstrap, reliability, nonparametric estimators.

PDF (Español)

Author Biography

Javier Ramírez-Montoya

Departamento de matemáticas y física, Grupo investigación en estadística y aplicaciones. GINESAP

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