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Showing 4 results for Parametric Bootstrap
Ehsan Kharati Koopaei, Soltan Mohammad Sadooghi Alvandi, Volume 8, Issue 1 (9-2014)
Abstract
The coefficient of variation is often used for comparing the dispersions of populations that have different measurement systems. In this study, the problem of testing the equality of coefficients of variation of several Normal populations is considered and a new test procedure based on Wald test and parametric bootstrap approach is developed. Since all the proposed tests for this problem are approximate, it is important to investigate how well each test controls the type I error rate. Therefore, via a simulation study, first the type I error rate of our new test is compared with some recently proposed tests. Then, the power of our proposed test is compared with others.
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.
Marjan Rajabi, Volume 14, Issue 1 (8-2020)
Abstract
The advent of new technology in recent years has facilitated the production of high dimension data. In these data we need evaluating more than one assumption. Multiple testing can be used for the collection of assumptions that are simultaneously tested and controlled the rate of family wise error that is the most critical issue in such tests. In this report, the authors apply Sidak and Stepwise strategies for controlling family wise error rate in detecting outlier profiles and comparing to each other. Considering our simulation results, the performance of such methods are compared using the parametric bootstrap snd by applying on real data in dataset illustrate the implementation of the proposed methods.
Ahad Malekzadeh, Asghar Esmaeli-Ayan, Seyed Mahdi Mahmodi, Volume 15, Issue 1 (9-2021)
Abstract
The panel data model is used in many areas, such as economics, social sciences, medicine, and epidemiology. In recent decades, inference on regression coefficients has been developed in panel data models. In this paper, methods are introduced to test the equality models of the panel model among the groups in the data set. First, we present a random quantity that we estimate its distribution by two methods of approximation and parametric bootstrap. We also introduce a pivotal quantity for performing this hypothesis test. In a simulation study, we compare our proposed approaches with an available method based on the type I error and test power. We also apply our method to gasoline panel data as a real data set.
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