Statistical inference of inverted Nadarajah–Haghighi distribution under type-II generalized hybrid censoring competing risks data

Tahir et al. (J Stat Comput Simul 88(14):2775–2798, 2018) introduced the inverse Nadarajah–Haghighi distribution (INHD) and demonstrated its ability to model positive real data sets with decreasing and upside-down bathtub hazard rate shapes. This article focuses on the inference of unknown parameter...

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Bibliographic Details
Published inJournal of engineering mathematics Vol. 144; no. 1
Main Authors Abushal, Tahani A., AL-Zaydi, Areej M.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.02.2024
Springer Nature B.V
Subjects
Algorithms
Applications of Mathematics
Computational Mathematics and Numerical Analysis
Confidence intervals
Datasets
Markov chains
Mathematical and Computational Engineering
Mathematical Modeling and Industrial Mathematics
Mathematical models
Mathematics
Mathematics and Statistics
Parameters
Statistical analysis
Statistical inference
Theoretical and Applied Mechanics
Bayes estimate
62N01
GT-II HCS
Bootstrap CI
Maximum likelihood estimate (MLE)
Credible CI
MCMC
62F10
Competing risks
Inverted Nadarajah Haghighi distribution (INHD)
HPD
62F15
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Summary:Tahir et al. (J Stat Comput Simul 88(14):2775–2798, 2018) introduced the inverse Nadarajah–Haghighi distribution (INHD) and demonstrated its ability to model positive real data sets with decreasing and upside-down bathtub hazard rate shapes. This article focuses on the inference of unknown parameters using a generalized Type-II hybrid censoring scheme (GT-II HCS) for the INHD in the presence of competing risks. The maximum likelihood (ML) and Bayes approaches are used to estimate the model parameters. Based on the squared error loss function, we compute Bayes estimates using Markov Chain Monte Carlo (MCMC) by applying Metropolis-Hasting (M-H) algorithm. Furthermore, the asymptotic confidence intervals, bootstrap confidence intervals (BCIs) and the highest posterior density (HPD) credible intervals are constructed. Using real data sets and simulation studies, we examined the introduced methods of inference with different sample sizes.
ISSN:0022-0833
1573-2703
DOI:10.1007/s10665-023-10331-1