Exponentiated half logistic distribution: Different estimation methods and joint confidence regions

Gui, Wenhao

doi:10.1080/03610918.2015.1122053

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…Özbilen and Genç [9] introduced the unit exponentiated-half logistic (UEHL) distribution corresponding to the omega distribution with a special case of the parameter value of the distribution. The distribution corresponds to the exponentiated half-logistic distribution with a simple transformation, which has many applications in reliability theory [10,11].…”

Section: Introductionmentioning

confidence: 99%

An Extension of the UEHL Distribution Based on the DUS Transformation

GENÇ,

ÖZBİLEN

2023

Journal of New Theory

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In this study, we propose a new distribution based on the Dinesh, Umesh, and Sanjay (DUS) transformation by using the Unit Exponentiated Half-Logistic (UEHL) distribution as the baseline distribution, a member of the family of proportional hazard rate models. Moreover, we study several properties, such as moments, skewness, kurtosis, stress-strength reliability, and likelihood ratio ordering. Further, we discuss the statistical inference on the parameters of the proposed distribution by the maximum likelihood estimation (MLE) method. Besides, we conduct a simulation based on the new distribution to investigate the behavior of the maximum likelihood estimates in various conditions. Furthermore, we present a numerical example to show the performance of the distribution on a real-life data set. Finally, we discuss the need for further research.

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Section: Introductionmentioning

confidence: 99%

An Extension of the UEHL Distribution Based on the DUS Transformation

GENÇ,

ÖZBİLEN

2023

Journal of New Theory

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“…Seo and Kang [25] examined the moment and ML estimators of the EHLD parameters. EHLD's ML, inverse moment, and modified inverse moment estimators, as well as the joint confidence regions were investigated by Gui [26]. Naidu et al [27] created a reliability test strategy for the EHLD.…”

Section: Motivation and Introductionmentioning

confidence: 99%

Bayesian and Non-Bayesian Inference for Unit-Exponentiated Half-Logistic Distribution with Data Analysis

et al. 2022

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Unit distributions are typically used in probability theory and statistics to illustrate useful quantities with values between zero and one. In this paper, we investigated an appropriate transformation to propose the unit-exponentiated half-logistic distribution (UEHLD), which is also beneficial for modelling data on the unit interval. This distribution’s mathematical features are supplied, including moments, probability-weighted moments, incomplete moments, various entropy measures, and stress–strength reliability. Using well-known estimation techniques such as the maximum likelihood, maximum product of spacing, and Bayesian inference, the estimators of the parameters relevant to the proposed distribution were determined. A comprehensive simulation analysis is provided to examine the performance of parameter estimation approaches on finite samples. The proposed distribution was realistically applied to data on economic growth and data on the tensile strength of polyester fibers to provide an explanation. Furthermore, the analysis of COVID-19 data from Britain as a medical statistical dataset is provided. The experimental results demonstrate that the suggested UEHLD yields a better comparison with some new unit distributions, as well as other unbounded distributions.

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“…The maximum likelihood estimation of the scale parameter in an exponentiated half logistic distribution based on progressively Type-II censored samples have been analyzed by [20]. [21] discussed the maximum likelihood estimation, inverse moment estimation and modified inverse moment estimation for the generalized half logistic distribution and construct the joint confidence regions for the parameters. The mixture of the Poisson distribution and generalized half logistic distribution based on minimum order statistic from GHL have been considered by [22].…”

Section: Introductionmentioning

confidence: 99%

A New Three Parameter Lifetime Model: The Complementary Poisson Generalized Half Logistic Distribution

Muhammad

Liu

2021

IEEE Access

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We propose a new model with flexible failure rate called complementary Poisson generalized half logistic (CPGHL). Various properties of the model are explored and examine numerically such as the explicit expressions of the moments, mean deviations, Bonferroni and Lorenz curves, Shannon and Renyi entropy. The distribution of mixture of two CPGHL and some related models based on the log-transform of CPGHL are discussed. The asymptotic of moments of residual life and asymptotic distribution of order statistics are obtained. The characterization of Poisson half logistic (PHL) by truncated moments of a certain function of a random variable is discussed. Estimation of the model parameters was approached by maximum likelihood, least square, and percentile methods. Further, the estimation by maximum likelihood for right censored data of the model were considered. The proposed estimation techniques were assessed by simulation studies. Three data applications are provided one of them is a censored data to demonstrate how the new model outperforms some other existing distribution in practice.INDEX TERMS generalized (exponentiated) half logistic model, least square estimation, maximum likelihood estimation, moments, moments residual life, percentile method of estimation, Renyi entropy, Shannon entropy

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Exponentiated half logistic distribution: Different estimation methods and joint confidence regions

Cited by 20 publications

References 10 publications

An Extension of the UEHL Distribution Based on the DUS Transformation

An Extension of the UEHL Distribution Based on the DUS Transformation

Bayesian and Non-Bayesian Inference for Unit-Exponentiated Half-Logistic Distribution with Data Analysis

A New Three Parameter Lifetime Model: The Complementary Poisson Generalized Half Logistic Distribution

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