scholarly journals Bayesian and maximum likelihood estimations of the Dagum parameters under combined-unified hybrid censoring

2021 ◽  
Vol 18 (3) ◽  
pp. 2930-2951
Author(s):  
Walid Emam ◽  
◽  
Khalaf S. Sultan
Keyword(s):  
Maximum Likelihood ◽  
Hybrid Censoring ◽  
Maximum Likelihood Estimations

scholarly journals Stochastic Analysis of Gaussian Processes via Fredholm Representation

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Tommi Sottinen ◽  
Lauri Viitasaari
Keyword(s):  
Brownian Motion ◽  
Maximum Likelihood ◽  
Differential Equations ◽  
Gaussian Process ◽  
Stochastic Differential Equations ◽  
Gaussian Processes ◽  
Stochastic Analysis ◽  
Series Expansions ◽  
Transfer Principle ◽  
Maximum Likelihood Estimations

We show that every separable Gaussian process with integrable variance function admits a Fredholm representation with respect to a Brownian motion. We extend the Fredholm representation to a transfer principle and develop stochastic analysis by using it. We show the convenience of the Fredholm representation by giving applications to equivalence in law, bridges, series expansions, stochastic differential equations, and maximum likelihood estimations.

scholarly journals Estimation of the Inverse Weibull Distribution Parameters under Type-I Hybrid Censoring

2021 ◽  
Vol 50 (5) ◽  
pp. 38-51
Author(s):  
Mohammad Kazemi ◽  
Mina Azizpoor
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Maximum Likelihood Estimates ◽  
Type I ◽  
Unknown Parameters ◽  
Bayes Estimates ◽  
Hybrid Censoring ◽  
Inverse Weibull Distribution ◽  
Distribution Parameters ◽  
Highest Posterior Density

The hybrid censoring is a mixture of type-I and type-II censoring schemes. This paper presents the statistical inferences of the inverse Weibull distribution parameters when the data are type-I hybrid censored. First, we consider the maximum likelihood estimates of the unknown parameters. It is observed that the maximum likelihood estimates can not be obtained in closed form. We further obtain the Bayes estimates and the corresponding highest posterior density credible intervals of the unknown parameters under the assumption of independent gamma priors using the importance sampling procedure. We also compute the approximate Bayes estimates using Lindley's approximation technique. The performance of the Bayes estimates have been compared with maximum likelihood estimates through the Monte Carlo Markov chain techniques. Finally, a real data set have been analysed for illustration purpose.

scholarly journals Inference for Step-Stress Partially Accelerated Life Test Model with an Adaptive Type-I Progressively Hybrid Censored Data

2021 ◽  
Vol 19 (1) ◽  
pp. 2-20
Author(s):  
Showkat Ahmad Lone ◽  
Ahmadur Rahman ◽  
Tanveer A. Tarray
Keyword(s):  
Rayleigh Distribution ◽  
Accelerated Life Test ◽  
Model Parameters ◽  
Type I ◽  
Test Model ◽  
Hybrid Censoring ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Life Tests ◽  
Maximum Likelihood Estimations

Consider estimating data of failure times under step-stress partially accelerated life tests based on adaptive Type-I hybrid censoring. The mathematical model related to the lifetime of the test units is assumed to follow Rayleigh distribution. The point and interval maximum-likelihood estimations are obtained for distribution parameter and tampering coefficient. Also, the work is conducted under a traditional Type-I hybrid censoring plan (scheme). A Monte Carlo simulation algorithm is used to evaluate and compare the performances of the estimators of the tempering coefficient and model parameters under both progressively hybrid censoring plans. The comparison is carried out on the basis of mean squared errors and bias.

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