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Showing 2 results for Mahdizadeh
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, 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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