Skip to content
Research Article Open access CC BY 4.0

Prediction for Modified Topp Leone-Chen Distribution Based on Progressive Type-II Censoring Scheme

G. R. AL-Dayian, A. A. EL-Helbawy, N. T. AL-Sayed, E. M. Swielum

Journal of Advances in Mathematics and Computer Science · pp. 33–57 · Published 17 Apr 2021

10.9734/jamcs/2021/v36i330345

Abstract

Prediction of future events on the basis of the past and present information is a fundamental problem of statistics, arising in many contexts and producing varied solutions. The predictor can be either a point or an interval predictor. This paper focuses on predicting the future observations from the modified Topp-Leone Chen distribution based on progressive Type-II censored scheme. The two-sample prediction is applied to obtain the maximum likelihood, Bayesian and E-Bayesian prediction (point and interval) for future order statistics. The Bayesian and E-Bayesian predictors are considered based on two different loss functions, the balanced squared error loss function; as a symmetric loss function and balanced linear exponential loss function; as an asymmetric loss function. The predictors are obtained based on conjugate gamma prior and uniform hyperprior distributions. A numerical example is provided to illustrate the theoretical results and an application using real data sets are used to demonstrate how the results can be used in practice.

Modified Topp-Leone Chen distribution progressive Type-II censored samples balanced squared error loss function balanced linear exponential loss function two-sample prediction maximum likelihood, Bayesian and E-Bayesian prediction Markov Chain Monte Carlo method

References (30)

  1. 1 Applied Life Data Analysis. [DOI]
  2. 2 A Simple Simulational Algorithm for Generating Progressive Type-II Censored Samples [DOI]
  3. 3 Bayesian and Non-Bayesian Estimation Using Balanced Loss Functions [DOI]
  4. 4 Bayes estimation based on -record data from a general class of distributions under balanced type loss functions [DOI]
  5. 5 E-Bayesian estimation for the Lomax distribution based on type-II censored data [DOI]
  6. 6 E-Bayesian estimation of failure probability and its application [DOI]
  7. 7 A new two sample type-II progressive censoring scheme [DOI]
  8. 8 E-Bayesian Estimation of Chen Distribution Based on Type-I Censoring Scheme [DOI]
  9. 9 E-Bayesian estimation and its E-posterior risk of the exponential distribution parameter based on complete and type I censored samples [DOI]
  10. 10 Estimation of the Generalized Power Weibull Distribution Parameters Using Progressive Censoring Schemes
  11. 11 Prediction of future failures for generalized exponential distribution under Type-I or Type-II hybrid censoring [DOI]
  12. 12 E-Bayesian Estimation for Burr-X Distribution Based on Generalized Type-I Hybrid Censoring Scheme [DOI]
  13. 13 E-Bayesian estimation of the exponentiated distribution family parameter under LINEX loss function [DOI]
  14. 14 E-Bayesian Estimation Based on Burr-X Generalized Type-II Hybrid Censored Data [DOI]
  15. 15 E-Bayesian analysis of the Gumbel type-ii distribution under type-ii censored scheme [DOI]
  16. 16 Inference and optimal design of multiple constant-stress testing for generalized half-normal distribution under type-II progressive censoring [DOI]
  17. 17 E-Bayesian Estimation of Two-Component Mixture of Inverse Lomax Distribution Based on Type-I Censoring Scheme [DOI]
  18. 18 E-Bayesian Estimation Based On Generalized Half Logistic Progressive Type-II Censored Data [DOI]
  19. 19 Bayesian and E-Bayesian estimation for the Kumaraswamy distribution based on type-ii censoring [DOI]
  20. 20 E-Bayesian estimations for the cumulative hazard rate and mean residual life based on exponential distribution and record data [DOI]
  21. 21 Statistical Inference for Topp–Leone-generated Family of Distributions Based on Records [DOI]
  22. 22 E-Bayesian Estimation for the Exponential Model Based on Record Statistics [DOI]
  23. 23 E-Bayesian and Hierarchical Bayesian Estimations Based on Dual Generalized Order Statistics from the Inverse Weibull Model [DOI]
  24. 24 Reliability analysis of Birnbaum–Saunders model based on progressive type-II censoring [DOI]
  25. 25 Statistical Inference for the Weighted Exponential Distribution under Progressive Type-II Censoring with Binomial Removal [DOI]
  26. 26 E-Bayesian Prediction for the Burr XII Model Based on Type-II Censored Data with Two Samples [DOI]
  27. 27 E-Bayesian Estimation for Rayleigh Model Using Progressive Type-II Censoring Data [DOI]
  28. 28 Bayesian Survival Analysis for Generalized Pareto Distribution Under Progressively Type II Censored Data [DOI]
  29. 29 Genetik algoritma ile ilerleyen tür tip 2 sansürlü örneklemlerde Weibull dağılımının parametrelerinin en çok olabilirlik tahmini [DOI]
  30. 30 E-Bayesian Estimation of the Parameter of the Logarithmic Series Distribution [DOI]

Cited by 2

Bayesian and E-Bayesian Estimation for a Modified Topp Leone-Chen Distribution Based on a Progressive Type-II Censoring Scheme

Zakiah I. Kalantan, E. M. Swielum, N. AL-Sayed · Symmetry · 2024

Showing 1 of 2 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

31

Page views

0

PDF downloads

4

Outbound clicks

2

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.