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This work introduces a parametric finite mixture model (FMM) approach to analyze the
dependent competing risks data subjected to progressive first-failure censoring and multiple
causes of failure. The cause-specific failure times are assumed to be flexibly modeled by the
Lehmann family of distributions (also known as the exponentiated distributions) with variation
in both distribution parameters. Application of the expectation maximization (EM) algorithm
facilitates the maximum likelihood estimation of the model parameters and illuminates the
contribution of the censored data. For interval estimation purposes, we resort to using the
asymptotic confidence intervals based on the observed Fisher information matrix. Practitioners
often prefer employing simpler lifetime distribution in order to facilitate the data modeling
process while knowing the true distribution. In this context, the effects of model misspecification
are studied based on the p-th quantile when the true distribution is misspecified. An
an extensive simulation study is performed to validate our proposed model. Finally, an automotive
warranty claims data set is used as an illustration to study the effectiveness of our proposed
model, assuming some important members of the Lehmann family, like generalized exponential
and exponentiated Pareto distributions. |