Ansa Alphonsa, Antony; Dr.Sankaran, P G(Cochin University of Science and Technology, September 1, 2005)
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Abstract:
So far, in the bivariate set up, the analysis of lifetime (failure time) data with
multiple causes of failure is done by treating each cause of failure separately. with
failures from other causes considered as independent censoring. This approach is
unrealistic in many situations. For example, in the analysis of mortality data on
married couples one would be interested to compare the hazards for the same cause
of death as well as to check whether death due to one cause is more important for the
partners’ risk of death from other causes. In reliability analysis. one often has
systems with more than one component and many systems. subsystems and
components have more than one cause of failure. Design of high-reliability systems
generally requires that the individual system components have extremely high
reliability even after long periods of time. Knowledge of the failure behaviour of a
component can lead to savings in its cost of production and maintenance and. in
some cases, to the preservation of human life. For the purpose of improving
reliability. it is necessary to identify the cause of failure down to the component
level. By treating each cause of failure separately with failures from other causes
considered as independent censoring, the analysis of lifetime data would be
incomplete. Motivated by this. we introduce a new approach for the analysis of
bivariate competing risk data using the bivariate vector hazard rate of Johnson and
Kotz (1975).
Description:
Department of Statistics, Cochin University of Science and Technology