CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Statistical Inference for Pareto Distribution based on Progressive Type-I Hybrid Censoring Scheme

by M. M. Mohie El-Din, A. R. Shafay, M. Nagy, A. Gamal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 4
Year of Publication: 2017
Authors: M. M. Mohie El-Din, A. R. Shafay, M. Nagy, A. Gamal
10.5120/ijca2017915802

M. M. Mohie El-Din, A. R. Shafay, M. Nagy, A. Gamal . Statistical Inference for Pareto Distribution based on Progressive Type-I Hybrid Censoring Scheme. International Journal of Computer Applications. 178, 4 ( Nov 2017), 1-8. DOI=10.5120/ijca2017915802

@article{ 10.5120/ijca2017915802,
author = { M. M. Mohie El-Din, A. R. Shafay, M. Nagy, A. Gamal },
title = { Statistical Inference for Pareto Distribution based on Progressive Type-I Hybrid Censoring Scheme },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2017 },
volume = { 178 },
number = { 4 },
month = { Nov },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number4/28659-2017915802/ },
doi = { 10.5120/ijca2017915802 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:49:27.379758+05:30
%A M. M. Mohie El-Din
%A A. R. Shafay
%A M. Nagy
%A A. Gamal
%T Statistical Inference for Pareto Distribution based on Progressive Type-I Hybrid Censoring Scheme
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 4
%P 1-8
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the maximum likelihood and Bayesian estimations are developed based on progressive Type-I hybrid censored sample from the Pareto distribution. The Bayesian estimators for the unknown parameters are computed using the squared error loss function. Also, the point and interval Bayesian predictions for the unobserved failures from the same sample and that from the future sample are derived. Moreover, a Monte Carlo simulation study is carried out to compare the performance of the maximum likelihood and the Bayesian estimators. Finally, numerical example is presented for illustrating all the inferential procedures developed here.

References
  1. R. Aggarwala, N. Balakrishnan,(1998). Some properties of progressive censored order statistics from arbitrary and uniform distributions with applications to inference and simulation. J.of Stat. Plan. and Infer. 70, 35-49.
  2. E. Cramer, G. Iliopoulos,(2010). Adaptive progressive Type- II ensoring, Test. 19, 342-358.
  3. M. Z. Raqab, A. Asgharzadeh, R. Valiollahi, (2010). Prediction for Pareto distribution based on progressively Type-II censored samples, Comput. Stat. – Data Anal. 54, 1732-1743.
  4. M. M. Mohie El-Din, A. R. Shafay, (2013). One-and twosample Bayesian prediction intervals based on progressively Type-II censored data, Stat. Papers. 54, 287-307.
  5. N. Balakrishnan, A. C. Cohen, (2014). Order statistics - inference: estimation methods. Elsevier .
  6. D. Kundu, A. Joarder, (2006). Analysis of Type - II progressively hybrid censored data, Comput. Stat.– Data Anal. 50, 2509-2528.
  7. A. Childs, B. Chandrasekar, N. Balakrishnan,(2008). Exact likelihood inference for an exponential parameter under progressive hybrid censoring schemes, In Stat. models and methods for biomedical and tech. Sys. 319-330.
  8. C.T. Lin, C.C. Chou, Y.L. Huang,(2012). Inference for the Weibull distribution with progressive hybrid censoring, Comput. Stat. – Data Anal. 56, 451-467.
  9. C.T. Lin, Y.L. Huang,(2012). On progressive hybrid censored exponential distribution, J.of Stat. Comput. and Simul. 82, 689-709.
  10. F. Hemmati, E. Khorram,(2013). Statistical analysis of the log-normal distribution under Type-II progressive hybrid censoring schemes, Commun. in Stat.-Simul. and Comput. 42, 52-75.
  11. V. Pareto,(1897). Cours d’Economie Politique, Rouge et Cie, Paris.
  12. H.T. Davis, M.L. Feldstein,(1979). The generalized Pareto law as a model for progressively censored survival data, Biometrika, 66, 299-306.
  13. A.C. Cohen, B.J. Whitten,(1988). Parameter Estimation in Reliability and Life Span Models, Marcel Dekker, Inc, New York .
  14. S. D. Grimshaw, (1993). Computing maximum likelihood estimates for the generalized Pareto distribution, Technometrics. 35, 185-191.
  15. T. Lwin,(1972). Estimation of the tail of the Paretian law, Scandinavian Actuarial J. 55 , 170-178.
  16. B.C. Arnold, S. J. Press, (1989). Bayesian estimation and prediction for Pareto data. J.of the American Stat. Association, 84, 1079-1084.
  17. I. Basak, P. Basak, N. Balakrishnan, (2006). On some predictors of times to failure of censored items in progressively censored samples. Comput. stat. – Data Anal, 50, 1313-1337.
  18. N. Balakrishnan, A. Childs, B. Chandrasekar,(2002). An efficient computational method for moments of order statistics under progressive censoring. Stat. – Probability Letters, 60,359-365.
  19. N. Balakrishnan, R. Aggarwala, (2000). Progressive Censoring: Theory, Methods and Applications, Birkhuser, Boston.
Index Terms

Computer Science
Information Sciences

Keywords

Bayesian estimation Bayesian prediction Pareto distribution Maximum likelihood estimation progressive hybrid censoring sample

Powered by PhDFocusTM