Analysis of masked data in a dependent competing risks model under unknown environment

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

System failure data is often analyzed to estimate component reliabilities. Due to cost and time constraints, the exact component causing the failure of the system cannot be identified in some cases. This phenomenon is called masking. Further, it is sometimes necessary for us to take account of the influence of the operating environment. Here we consider a series system, operating under unknown environment, of two components whose failure times follow the Marshall-Olkin bivariate exponential distribution. We present a maximum likelihood approach for obtaining estimators from the masked data for this system. From a simulation study, we found that the relative errors of the estimates are almost well behaved even for small or moderate expected number of systems whose cause of failure is identified.

Original languageEnglish
Pages (from-to)1579-1593
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Volume31
Issue number9
DOIs
Publication statusPublished - 1 Jan 2002

Keywords

  • Bivariate exponential distribution
  • Masking probability
  • Maximum likelihood estimation
  • Shock model

Fingerprint Dive into the research topics of 'Analysis of masked data in a dependent competing risks model under unknown environment'. Together they form a unique fingerprint.

Cite this