Parameter estimations for generalized exponential distribution under progressive type-I interval censoring

On Estimating Flexible Weibull Parameters with Type I Progressive Interval Censoring with Random Removal Using Data of Cancerous Tumors in Blood

“…Therefore, the MLE of can be found by maximizing directly, that is, Therefore, the maximum likelihood estimation of parameter is given by (10) It may be noted that (9) and (10) cannot be solved simultaneously to provide a nicely closed form for the estimators. Therefore, we propose to use fixed point iteration method for solving these equations.…”

“…Based on the progressive type-I interval censored sampling, Ashour & Afify [7] derived the maximum likelihood estimators of parameters of the exponentiated Weibull family and their asymptotic variances under random removal. Lin et al [8] determined optimally spaced inspection times for the log-normal distribution, while Ng & Wang [9] and Chen & Lio [10] compared three classical estimation methods, the maximum In Bayesian approach, It is too difficult to find integrate over the posterior distribution and the problem is that the integrals are usually impossible to evaluate analytically. But in MCMC technique, the MCMC methodology provided a convenient and efficient way to sample from complex, high-dimensional statistical distributions.…”

Estimation of the Unknown Parameters for the Compound Rayleigh Distribution Based on Progressive First-Failure-Censored Sampling

“…Newton-Raphson iteration is employed to solve (12). The corresponding MLE's of the reliability function , and hazard rate function ( ) S t ( ) H t , are given respectively by (3) and (4) after replacing  and  by their MLE's  and  .…”

“…[1] mentioned that the inference is feasible, and practical when the sample data are gathered according to a Type-II progressively censored study experimental scheme. Statistical inferences on the parameters of failure time distributions under progressive censoring have been studied by several authors such as [1][2][3][4][5][6][7][8][9][10][11][12]. A recent account on progressive censoring schemes can be found in the book by [13], or in the excellent review by [14].…”

Estimations of the parameters of generalised exponential distribution under progressive interval type-I censoring scheme with random removals

“…the experiment is terminated at this stage. Recently Chen and Lio (2010) proposed a methodology to estimate parameters involve in GED under PTI interval censoring under the assumption that the proportions (p i ) of the patients leaving the experiment during (T i−1 , T i ] is known in advance, i.e. they prefixed the proportions p 1 , p 2 , .…”