AbstractIn this paper, maximum likelihood and Bayes estimates of the parameters for Burr type X distribution based on doubly type II censored sample of dual generalized order statistics are obtained. Two different cases are considered. In the first case, the shape parameter is estimated when the scale parameter is known while in the second case the estimators for scale and shape parameters are obtained when both parameters are assumed to be unknown. For Bayesian estimation, Monte Carlo Integration is used to improve the approximation of resulting integrals. Simulation studies are conducted to demonstrate the efficiency of the proposed methods through two special cases
In this article, we consider the estimation of P[Y < X], when strength, X and stress, Y are two inde...
[[abstract]]This study considers the estimation of a two-parameter Burr-XII distribution under Type ...
This paper compares the robust and E-Bayesian estimations of the shape parameter for Burr XII distri...
AbstractIn this paper, maximum likelihood and Bayes estimates of the parameters for Burr type X dist...
The estimation of the parameters of Burr type III distribution based on dual generalized order stati...
The two parameter Burr type X distribution is considered and its scale parameter is estimated from a...
WOS: 000407117100025In this study, estimation and prediction problems for the Burr type III distribu...
The purpose of the paper is to address the problem of estimation and prediction of the Burr type XI ...
In this research, point and interval estimation for unknown parameters of Burr Type-X (Burr-X) distr...
AbstractMaximum likelihood and Bayes estimates for the two parameters and the reliability function o...
We develop three new models from the baseline Burr type X with two parameters distribution (BX) usin...
The paper deals with the maximum likelihood estimation of the parameters of the Burr type V distribu...
We propose the generalizations of Burr Type X distribution with two parameters by using the methods ...
In this paper, we study the statistical inference of the generalized inverted exponential distributi...
Abstract: In this paper,the estimation of stress-strength parameter R = P (Y<X) is considered Whe...
In this article, we consider the estimation of P[Y < X], when strength, X and stress, Y are two inde...
[[abstract]]This study considers the estimation of a two-parameter Burr-XII distribution under Type ...
This paper compares the robust and E-Bayesian estimations of the shape parameter for Burr XII distri...
AbstractIn this paper, maximum likelihood and Bayes estimates of the parameters for Burr type X dist...
The estimation of the parameters of Burr type III distribution based on dual generalized order stati...
The two parameter Burr type X distribution is considered and its scale parameter is estimated from a...
WOS: 000407117100025In this study, estimation and prediction problems for the Burr type III distribu...
The purpose of the paper is to address the problem of estimation and prediction of the Burr type XI ...
In this research, point and interval estimation for unknown parameters of Burr Type-X (Burr-X) distr...
AbstractMaximum likelihood and Bayes estimates for the two parameters and the reliability function o...
We develop three new models from the baseline Burr type X with two parameters distribution (BX) usin...
The paper deals with the maximum likelihood estimation of the parameters of the Burr type V distribu...
We propose the generalizations of Burr Type X distribution with two parameters by using the methods ...
In this paper, we study the statistical inference of the generalized inverted exponential distributi...
Abstract: In this paper,the estimation of stress-strength parameter R = P (Y<X) is considered Whe...
In this article, we consider the estimation of P[Y < X], when strength, X and stress, Y are two inde...
[[abstract]]This study considers the estimation of a two-parameter Burr-XII distribution under Type ...
This paper compares the robust and E-Bayesian estimations of the shape parameter for Burr XII distri...