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Showing 5 results for Naghizadeh Qomi
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.
Mehran Naghizadeh Qomi, Maryam Vahidian,
Volume 11, Issue 2 (3-2018)
Abstract
The problem of finding tolerance intervals receives very much attention in researches and is widely applied in industry. Tolerance interval is a random interval that covers a proportion of the considered population with a specified confidence level. In this paper, the statistical tolerance limits are expressed for lifetime of k out of n systems with exponentially distributed component lifetimes. Then, we compute the accuracy of proposed tolerance limits and the number of failures needed to attain a desired accuracy level based on type-II right censored data. Finally, we extend our results to the Weibull distribution.
Mehran Naghizadeh Qomi, Zohre Mahdizadeh, Hamid Zareefard,
Volume 12, Issue 1 (9-2018)
Abstract
Suppose that we have a random sample from one-parameter Rayleigh distribution. In classical methods, we estimate the interesting parameter based on the sample information and with usual estimators. Sometimes in practice, the researcher has some information about the unknown parameter in the form of a guess value. This guess is known as nonsample information. In this case, linear shrinkage estimators are introduced by combining nonsample and sample information which have smaller risk than usual estimators in the vicinity of guess and true value. In this paper, some shrinkage testimators are introduced using different methods based on vicinity of guess value and true parameter and their risks are computed under the entropy loss function. Then, the performance of shrinkage testimators and the best linear estimator is calculated via the relative efficiency of them. Therefore, the results are applied for the type-II censored data.
Mehran Naghizadeh Qomi,
Volume 14, Issue 2 (2-2021)
Abstract
In classical statistics, the parameter of interest is estimated based on sample information and using natural estimators such as maximum likelihood estimators. In Bayesian statistics, the Bayesian estimators are constructed based on prior knowledge and combining with it sample information. But, in some situations, the researcher has information about the unknown parameter as a guess. Bayesian shrinkage estimators can be constructed by Combining this non-sample information with sample information together with the prior knowledge, which is in the area of semi-classical statistics. In this paper, we introduce a class of Bayesian shrinkage estimators for the Weibull scale parameter as a generalization of the estimator at hand and consider the bias and risk of them under LINEX loss function. Then, the proposed estimators are compared using a real data set.
Mehran Naghizadeh Qomi, Zohre Mahdizadeh,
Volume 19, Issue 1 (9-2025)
Abstract
This paper investigates repetitive acceptance sampling inspection plans of lots based on type I censoring when the lifetime has a Tsallis q-exponential distribution. A repetitive acceptance sampling inspection plan is introduced, and its components, along with the optimal average sample number and the operating characteristic value of the plan, are calculated under the specified values for the parameter of distribution and consumer's and producer's risks using a nonlinear programming optimization problem. Comparing the results of the proposed repetitive acceptance sampling plan with the optimal single sampling inspection plan demonstrates the efficiency of the repetitive acceptance sampling plan over the single sampling plan. Moreover, repetitive sampling plans with a limited linear combination of risks are introduced and compared with the existing plan. Results of the introduced plan in tables and figures show that this plan has a lower ASN and, therefore, more efficiency than the existing design. A practical example in the textile industry is used to apply the proposed schemes.
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