Statistical Inference for Alpha-Series Process with the Generalized Rayleigh Distribution
Arts and Sciences Faculty, Statistics Department, University of Kirikkale, Kirikkale 71450, Turkey
Received: 31 January 2019 / Revised: 19 April 2019 / Accepted: 26 April 2019 / Published: 30 April 2019
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In the modeling of successive arrival times with a monotone trend, the alpha-series process provides quite successful results. Both selecting the distribution of the first arrival time and making an optimal statistical inference play a crucial role in the modeling performance of the alpha-series process. In this study, when the distribution of the first arrival time is the generalized Rayleigh, the problem of statistical inference for the
parameters of the alpha-series process is considered. Further, in order to obtain optimal modeling performance from the mentioned alpha-series process, various estimators for the model parameters are obtained by employing different estimation methodologies such as maximum likelihood, modified maximum spacing, modified least-squares, modified moments, and modified L-moments. By a series of Monte Carlo simulations, the estimation efficiencies of the obtained estimators are evaluated through the different sample sizes. Finally, two real datasets are analyzed to illustrate the importance of modeling with the alpha-series process.
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Demirci Biçer, H. Statistical Inference for Alpha-Series Process with the Generalized Rayleigh Distribution. Entropy 2019, 21, 451.
Demirci Biçer H. Statistical Inference for Alpha-Series Process with the Generalized Rayleigh Distribution. Entropy. 2019; 21(5):451.
Demirci Biçer, Hayrinisa. 2019. "Statistical Inference for Alpha-Series Process with the Generalized Rayleigh Distribution." Entropy 21, no. 5: 451.
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