I S S N 2 3 4 7 -1 9 2 1 V o l u m e 1 2 N u m b e r 1 2 J o u r n a l o f A d v a n c e s i n M a t h e m a t i c s 6863 | P a g e J a n u a r y 2 0 1 7 w w w . hasaballahmohamed@yahoo.com ABSTRACT This paper aims to estimate the unknown parameters, survival and hazard functions for exponentiated Rayleigh distribution based on unified hybrid censored data. The maximum likelihood estimators (MLEs), Bayes, and parametric bootstrap methods are used for estimating the two unknown parameters as well as survival and hazard functions. We propose to apply Markov chain Monte Carlo (MCMC) technique to carry out a Bayesian estimation procedure. Approximate confidence intervals for the unknown parameters moreover survival and hazard functions are constructed based on the s-normal approximation to the asymptotic distribution of MLEs. The approximate Bayes estimators have been obtained under the assumptions of informative and non-informative priors depending on symmetric and asymmetric loss functions via the Gibbs within Metropolis-Hasting samplers procedure. Finally, the proposed methods can be understood through illustrating the results of the real data analysis.