Abstract
In the present paper, a new family of lifetime distributions is introduced according to cumulative hazard rate function, the well-known concept in survival analysis and reliability engineering. Some important properties of proposed model including survival function, quantile function, hazard function, order statistic and some results of stochastic ordering are obtained in general setting. An especial case of this new family is introduced by considering Weibull distribution as the parent distribution; in addition estimating unknown parameters of specialized model will be examined from the perspective of Bayesian and classic statistics. Moreover, three examples of real data sets: complete, right-censored and progressively type-I interval-censored data are studied; point and interval estimations of all parameters are obtained. Finally, the superiority of proposed model in terms of parent Weibull distribution over other fundamental statistical distributions is shown via complete real observations.
Highlights
The statistical distribution theory has been widely explored by researchers in recent years
An especial case of this new family is introduced by considering Weibull distribution as the parent distribution; in addition estimating unknown parameters of specialized model will be examined from the perspective of Bayesian and classic statistics
We introduce a New family of Lifetime distributions based on the Cumulative Hazard rate quantity of a parent distribution G, so-called N LCH G distribution
Summary
The statistical distribution theory has been widely explored by researchers in recent years. We ...rst obtain the fundamental and statistical properties of N LCH G in general setting and we propose an especial case of N LCH G model by considering Weibull distribution instead of the parent distribution G It is referred as N LCH W eibull (or N LCH W ) distribution. We consider Maximum likelihood, Bayesian and bootstrap estimation procedures in order to estimate the unknown parameters of the new model for complete, right-censored and progressively type-I interval-censored data sets.
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