Inference for two Lomax populations under joint type-II censoring
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Publication:5055171
DOI10.1080/03610918.2020.1814814OpenAlexW3084023969MaRDI QIDQ5055171
Publication date: 13 December 2022
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.02517
EM algorithmmaximum likelihood estimationBayesian estimationLomax distributionbootstrap confidence intervalstype-II censoringjoint censoring scheme
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- Exact likelihood inference for two exponential populations under joint type-II censoring
- Theoretical comparison of bootstrap confidence intervals
- Estimation of parameters from progressively censored data using EM algorithm
- Classical and Bayesian inferential approaches using Lomax model under progressively type-I hybrid censoring
- Joint records from two exponential populations and associated inference
- Estimating the parameters of an inverse Weibull distribution under progressive type-I interval censoring
- Point and interval estimation of Weibull parameters based on joint progressively censored data
- Conditional Maximum Likelihood and Interval Estimation for Two Weibull Populations under Joint Type-II Progressive Censoring
- Exact Likelihood Inference for Two Exponential Populations Under Joint Progressive Type-II Censoring
- Bayesian inference based on a jointly type-II censored sample from two exponential populations
- Estimation on Lomax progressive censoring using the EM algorithm
- Bayes Estimation Based on Joint Progressive Type II Censored Data Under LINEX Loss Function
- On a Generalized Savage Statistic with Applications to Life Testing
- Locally Most Powerful Rank Tests for the Two-sample Problem with Censored Data
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