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Showing 3 results for Goodness-of-Fit Test
Bahareh Afhami, Mohsen Madadi, Mohsen Rezapour, Volume 9, Issue 1 (9-2015)
Abstract
In this paper, first the Shannon entropy of k-record values is derived from the generalized Pareto distribution and propose goodness-of-fit tests based on this entropy. Finally, real data and a simulation study are used for analyzing the performance of this statistic.
Meysam Mohammadpour, Hossein Bevrani, Reza Arabi Belaghi, Volume 15, Issue 1 (9-2021)
Abstract
Wind speed probabilistic distributions are one of the main wind characteristics for the evaluation of wind energy potential in a specific region. In this paper, 3-parameter Log-Logistic distribution is introduced and it compared with six used statistical models for the modeling the actual wind speed data reported of Tabriz and Orumiyeh stations in Iran. The maximum likelihood estimators method via Nelder–Mead algorithm is utilized for estimating the model parameters. The flexibility of proposed distributions is measured according to the coefficient of determination, Chi-square test, Kolmogorov-Smirnov test, and root mean square error criterion. Results of the analysis show that 3-parameter Log-Logistic distribution provides the best fit to model the annual and seasonal wind speed data in Orumiyeh station and except summer season for Tabriz station. Also, wind power density error is estimated for the proposed different distributions.
, Hadi Alizadeh Noughabi, Majid Chahkandi, Volume 19, Issue 2 (4-2025)
Abstract
In today’s industrial world, effective maintenance plays a key role in reducing costs and improving productivity. This paper introduces goodness-of-fit tests based on information measures, including entropy, extropy, and varentropy, to evaluate the type of repair in repairable systems. Using system age data after repair, the tests examine the adequacy of the arithmetic reduction of age model of order 1. The power of the proposed tests is compared with classical tests based on martingale residuals and the probability integral transform. Simulation results show that the proposed tests perform better in identifying imperfect repair models. Their application to real data on vehicle failures also indicates that this model provides a good fit.
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