|
|
|
 |
Search published articles |
 |
|
Showing 8 results for Confidence Interval
Elham Zamanzadeh, Jafar Ahmadi,
Volume 5, Issue 1 (9-2011)
Abstract
In this paper, first a brief introduction of ranked set sampling is presented. Then, construction of confidence intervals for a quantile of the parent distribution based on ordered ranked set sample is given. Because the corresponding confidence coefficient is an step function, one may not be able to find the exact prescribed value. With this in mind, we introduce a new method and show that one can obtained an optimal confidence interval by appealing the proposed approach. We also compare the proposed scheme with the other existence methods.
Akbar Asgharzadeh, Mina Azizpour, Reza Valiollahi,
Volume 9, Issue 1 (9-2015)
Abstract
One of the drawbacks of the type II progressive censoring scheme is that the length of the experiment can be very large. Because of that, recently a new censoring scheme named as the type II progressively hybrid censored scheme has received considerable interest among the statisticians. In this paper, the statistical inference for the half-logistic distribution is discussed based on the progressively type II hybrid censored samples. The maximum likelihood estimator, the approximate maximum likelihood estimator and the Bayes estimator of parameter using Lindley approximation and MCMC method are obtained. Asymptotic confidence intervals, Bootstrap confidence intervals and Bayesian credible intervals are obtained. Different point and interval estimators are compared using Monte Carlo simulation. A real data set is presented for illustrative purposes.
Sana Eftekhar, Ehsan Kharati-Koopaei, Soltan Mohammad Sadooghi-Alvandi,
Volume 9, Issue 2 (2-2016)
Abstract
Process capability indices are widely used in various industries as a statistical measure to assess how well a process meets a predetermined level of production tolerance. In this paper, we propose new confidence intervals for the ratio and difference of two Cpmk indices, based on the asymptotic and parametric bootstrap approaches. We compare the performance of our proposed methods with generalized confidence intervals in term of coverage probability and average length via a simulation study. Our simulation results show the merits of our proposed methods.
Azadeh Kiapour, Mehran Naghizadeh Qomi,
Volume 10, Issue 2 (2-2017)
Abstract
In this paper, an approximate tolerance interval is presented for the discrete size-biased Poisson-Lindley distribution. This approximate tolerance interval, is constructed based on large sample Wald confidence interval for the parameter of the size-biased Poisson-Lindley distribution. Then, coverage probabilities and expected widths of the proposed tolerance interval is considered. The results show that the coverage probabilities have a better performance for the small values of the parameter and are close to the nominal confidence level, and are conservative for the large values of the parameter. Finally, an applicable example is provided for illustrating approximate tolerance interval.
Mohammad Reaz Kazemi,
Volume 14, Issue 2 (2-2021)
Abstract
In this paper, we investigate the confidence interval for the parameter of the common correlation coefficient of several bivariate normal populations. To do this, we use the confidence distribution approach. By simulation studies and using the concepts of coverage probability and expected length, We compare this method with the generalized variable approach. Results of simulation studies show that the coverage probability of the proposed method is close to the nominal level in all situations and also, in most cases, the expected length of this method is less than that of the generalized variable approach. Finally, we present two real examples to apply this approach.
Mr. Ali Rostami, Dr. Mohammad Khanjari Sadegh, Dr. Mohammad Khorashadizadeh,
Volume 16, Issue 2 (3-2023)
Abstract
In this article, we consider the estimation of R{r,k}= P(X{r:n1} < Y{k:n2}), when the stress X and strength Y are two independent random variables from inverse Exponential distributions with unknown different scale parameters. R{r,k} is estimated using the maximum likelihood estimation method, and also, the asymptotic confidence interval is obtained. Simulation studies and the performance of this model for two real data sets are presented.
Dr Adeleh Fallah,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, non-parametric inference is considered for $k$-component coherent systems, when the system lifetime data is progressively type-II censored. In these coherent systems, it is assumed that the system structure and system signature are known. Based on the observed progressively type-II censored, non-parametric confidence intervals are calculated for the quantiles of component lifetime distribution. Also, tolerance limits for component lifetime distribution are obtained. Non-parametric confidence intervals for quantiles and tolerance limits are obtained based on two methods, distribution function method and W mixed matrix method. Two numerical example is used to illustrate the methodologies developed in this paper.
Dr Adeleh Fallah,
Volume 19, Issue 1 (9-2025)
Abstract
In this paper, estimation for the modified Lindley distribution parameter is studied based on progressive Type II censored data. Maximum likelihood estimation, Pivotal estimation, and Bayesian estimation were calculated using the Lindley approximation and Markov chain Monte Carlo methods. Asymptotic, Pivotal, bootstrap, and Bayesian confidence intervals are provided. A Monte Carlo simulation study has been conducted to evaluate and compare the performance of different estimation methods. To further illustrate the introduced estimation methods, two real examples are provided.
|
|
