Statistical Inference under Censored Data for the New Exponential-X Fréchet Distribution: Simulation and Application to Leukemia Data

Algorithm 1

The MH algorithm can be known as an approximation method for evaluating integrals that cannot be evaluated explicitly.

(1)	Initiate using these values , , and where these values are the values evaluate form the MLEs
(2)	Start the loop with value
(3)	Simulate , and from normal proposal distribution , , and , repressively
(4)	Now, we must know the acceptance probability to determine which value will be stored and considered as the estimate of the parameter, so we will use the following ratio to calculate the acceptance probability: , , and
(5)	We will randomize and generate a random sample from the uniform distribution having a range from 0 to 1
(6)	If the generated value , we will consider , if not we will consider
(7)	In order to find the estimates for the other two parameters, we must make a repetition for Step (6) but for, and
(8)	Increase the loop counter by one such that
(9)	In order to find accurate approximation for the estimates, we must make a repetition for the steps from ((3)–(8)), repetitions for obtaining values for the parameters of the proposed distribution, and this sample can be written as the following: