Dergiler / Hacettepe Journal of Mathematics and Statistics / 2018 / Cilt: 47 - Sayı: 4

Parameters estimation for mixed generalized inverted exponential distributions with type-II progressive hybrid censoring

Sayfa
1023–1039
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Some simulations are implemented and a case of analysis is provided to illustrate the proposed method.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The type-II progressive hybrid censoring scheme, which is a mixture of type-II progressive and hybrid censoring schemes, has become substantially fashionable due to its flexibility of allowing for random removals of the remaining survival units at each failure time and terminatingthe experiment at a pre-specied time.
📊
Temel Bulgular & Veri:In the literature, this censoring scheme has been used to analyze lifetime data for general populationdistributions such as exponential distributions and Weibull distributions.
💡
Sonuç & Katkı:Some simulations are implemented and a case of analysis is provided to illustrate the proposed method.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Type-II progressive hybrid censoring · Mixed generalized inverted exponential distribution (MGIED) · EM algorithm

Abstract

The type-II progressive hybrid censoring scheme, which is a mixture of type-II progressive and hybrid censoring schemes, has become substantially fashionable due to its flexibility of allowing for random removals of the remaining survival units at each failure time and terminatingthe experiment at a pre-specied time. In the literature, this censoring scheme has been used to analyze lifetime data for general populationdistributions such as exponential distributions and Weibull distributions. However, we seldom focus on parameter estimations for the mixture distribution, which is an important class of models in reliability analysis. This paper aims to investigate the estimation problem of mixed generalized inverted exponential distribution (MGIED) under the type-II progressive hybrid censoring scheme. The maximum likelihood estimators (MLEs) of the model are obtained via EM algorithm. Some simulations are implemented and a case of analysis is provided to illustrate the proposed method.

🔖

Bu Makaleye Atıf Yap

TİAN, M., YANG, A., TİAN, Y., Li, E. (2022). Parameters estimation for mixed generalized inverted exponential distributions with type-II progressive hybrid censoring. Hacettepe Journal of Mathematics and Statistics, Cilt: 47 - Sayı: 4, 1023–1039. https://ajindex.com/articles/529873

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (14)

  1. Abouammoh, A. M. and Alshingiti, A. M. Reliability estimation of generalized inverted exponential distribution, Journal of Statistical Computation and Simulation 79 (11), 1301- 1315, 2009.
  2. A fy, W. M. Classical estimation of mixed Rayleigh distribution in type I progressive cen- sored, Journal of Statistical Theory and Applications 10 (4), 619-632, 2011.
  3. Balakrishnan, N. and Aggarwala, R. Progressive censoring: Theory, Methods and Applica- tions. Boston: Birkhauser., 2000.
  4. Balakrishnan, N., Habibi Rad, A. and Arghami, N. R. Testing exponentiality based on Kullback-Leibler information with progressively type-II censored data, IEEE Transactions on Reliability 56, 301-307, 2007.
  5. Balakrishnan, N., Kannan, N., Lin, C. T. and Wu, S.J. Inference for the extreme value distributions under progressive type-II censoring, Journal of Statistical Computation and Simulation, 74, 25-45, 2004.
  6. Balakrishnan, N. and Kundu, D. Hybrid censoring: Models, inferential results and applica- tions, Computational Statistics and Data Analysis 57 (1), 166-209, 2013.
  7. Basak, P. and Basak, I., Balakrishnan, N. Estimation for the three-parameter lognormal dis- tribution based on progressively censored data, Computational Statistics and Data Analysis 53 (10), 3580-3592, 2009.
  8. Chen, D. G. and Lio, Y. L. Parameter estimations for generalized exponential distribution under progressive type-I interval censoring, Computational Statistics and Data Analysis, 54, 1581-1591, 2010.
  9. Childs, A., Chandrasekar, B. and Balakrishnan, N. Exact likelihood inference for an expo- nential parameter under progressive hybrid censoring, Statistical Models and Methods for Biomedical and Technical Systems 319-330, 2008.
  10. Cho, Y., Sun, H., Lee, K. Exact likelihood inference for an exponential parameter under generalized progressive hybrid censoring scheme, Statistical Methodology 23, 18-34, 2015.
  11. Dempster, A. P., Laird, N. M. and Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm, Journal of Royal Statistical Society Series B 39 (1), 1-38, 1977.
  12. Dey, S., Pradhan, B. Generalized inverted exponential distribution under hybrid censoring, Statistical Methodology 18, 101-114, 2014.
  13. Gupta, R. D. and Kundu, D. Exponentiated Exponential Family: An Alternative to Gamma and Weibull Distributions, Computational Statistics and Data Analysis 43 (1), 117-130, 2001.
  14. Hashemi, M., Tavangar, M. and Asadi, M. Some properties of the residual lifetime of progres- sively type-II right censored order statistics, Stat