Statistical Inference for Geometric Process with the Power Lindley Distribution
Yükleniyor...
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The geometric process (GP) is a simple and direct approach to modeling of the successive inter-arrival time data set with a monotonic trend. In addition, it is a quite important alternative to the non-homogeneous Poisson process. In the present paper, the parameter estimation problem for GP is considered, when the distribution of the first occurrence time is Power Lindley with parameters alpha and lambda. To overcome the parameter estimation problem for GP, the maximum likelihood, modified moments, modified L-moments and modified least-squares estimators are obtained for parameters a, alpha and lambda. The mean, bias and mean squared error (MSE) values associated with these estimators are evaluated for small, moderate and large sample sizes by using Monte Carlo simulations. Furthermore, two illustrative examples using real data sets are presented in the paper.
Açıklama
Anahtar Kelimeler
geometric process, maximum likelihood estimate, modified moment estimate, modified L-moment estimate, modified least-square estimate
Kaynak
Entropy
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
20
Sayı
10
Künye
Bicer C. Statistical Inference for Geometric Process with the Power Lindley Distribution. Entropy. 2018; 20(10):723.
