Statistical Inference for Alpha-Series Process with the Generalized Rayleigh Distribution
Yükleniyor...
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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 alpha, beta, and gamma 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.
Açıklama
Anahtar Kelimeler
alpha-series process, geometric process, maximum likelihood estimate, modified maximum spacing estimate, modified least-squares estimate
Kaynak
Entropy
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
21
Sayı
5
Künye
Demirci Biçer H. Statistical Inference for Alpha-Series Process with the Generalized Rayleigh Distribution. Entropy. 2019; 21(5):451.
