"Identifiability and consistency in masking models
for competing risks data."
Abstract
We consider the estimation of the marginal
distributions with competing
risks data and masked failure cause (called MCR data).
The problem arises naturally when
the failure time and the cause are independent,
which is the case when the failure timess of different
components in the system are independent and
follow a proportional hazards model.
We investigate the identifiability issue for
two popular models in the literature on the MCR data.
A finding of the paper
is that the parameters in the
model proposed by Flehinger et al. (2001)
is not identifiable unless additional constraints are imposed
on the parameters. Thus the likelihood-based
statistical analyses such as that in
Mukhopadhyay (2006, p. 816)
based on the latter model in the literature
are erroneous.
We also show that the marginal MLE of the failure cause
under the other model
is consistent and efficient under certain regularity
assumptions. This is joint work with Qiqing Yu.
