A unit is said to be randomly censored when the information on time occurrence of an event is not available due to either\nloss to followup, withdrawal, or nonoccurrence of the outcome event before the end of the study. It is assumed in independent\nrandom/noninformative censoring that each individual has his/her own failure time T and censoring time C; however, one can only\nobserve the random vector, say, (X; ?).The classical approach is considered for analysing the generalised exponential distribution\nwith random or noninformative censored samples which occur most often in biological or medical studies. The Bayes methods\nare also considered via a numerical approximation suggested by Lindley in 1980 and that of the Laplace approximation procedure\ndeveloped by Tierney and Kadane in 1986 with assumed informative priors alongside linear exponential loss function and squared\nerror loss function. A simulation study is carried out to compare the estimators proposed in this paper. Two datasets have also been\nillustrated.
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