Lifetime Data Analysis

, Volume 20, Issue 4, pp 514–537

Nonparametric estimation with recurrent competing risks data

Authors

    • Elon University
  • Edsel A. Peña
    • 216 LeConte CollegeUniversity of South Carolina
Article

DOI: 10.1007/s10985-013-9280-6

Cite this article as:
Taylor, L.L. & Peña, E.A. Lifetime Data Anal (2014) 20: 514. doi:10.1007/s10985-013-9280-6

Abstract

Nonparametric estimators of component and system life distributions are developed and presented for situations where recurrent competing risks data from series systems are available. The use of recurrences of components’ failures leads to improved efficiencies in statistical inference, thereby leading to resource-efficient experimental or study designs or improved inferences about the distributions governing the event times. Finite and asymptotic properties of the estimators are obtained through simulation studies and analytically. The detrimental impact of parametric model misspecification is also vividly demonstrated, lending credence to the virtue of adopting nonparametric or semiparametric models, especially in biomedical settings. The estimators are illustrated by applying them to a data set pertaining to car repairs for vehicles that were under warranty.

Keywords

Recurrent eventsCompeting risksPerfect and partial repairsMartingalesSurvival analysisRepairable systemsNonparametric methods

Copyright information

© Springer Science+Business Media New York 2013