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Open AccessOn Estimating Multi- Stress Strength Reliability for Inverted Kumaraswamy Under Ranked Set Sampling with Application in Engineering
The terrible operating constraints of many real-world events cause systems to malfunction regularly. The failure of systems to perform their intended duties when they reach their lowest, highest, or both extre...
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Article
Bayesian Estimation of Stress Strength Modeling Using MCMC Method Based on Outliers
In reliability literature and engineering applications, stress-strength (SS) models are particularly important. This paper aims to estimate the SS reliability for an inverse Weibull distribution having the sam...
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Article
Bayesian Analysis of Generalized Inverted Exponential Distribution Based on Generalized Progressive Hybrid Censoring Competing Risks Data
In this study, a competing risk model was developed under a generalized progressive hybrid censoring scheme using a generalized inverted exponential distribution. The latent causes of failure were presumed to ...
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Article
Correction to: The Pareto–Poisson Distribution: Characteristics, Estimations and Engineering Applications
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Article
Classical and Bayesian Estimation of Entropy for Pareto Distribution in Presence of Outliers with Application
The measure of entropy has a pivotal role in the information theory area. In this paper, estimation of differential entropy for Pareto distribution in presence of r outliers is considered. In this regard, the ...
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Article
The Pareto–Poisson Distribution: Characteristics, Estimations and Engineering Applications
A new three-parameter lifetime distribution based on compounding Pareto and Poisson distributions is introduced and discussed. Various statistical and reliability properties of the proposed distribution includ...
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Article
Analysis of Progressive Type-II Competing Risks Data, with Applications
In this study, a competing risk (CR) model is studied under progressive Type-II censoring (PTIIC) lifetimes having the inverted Topp–Leone distribution. The latent causes of failure are presumed to be independ...
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Article
A New Class of Power Function Distribution: Properties and Applications
A new class of lifetime distributions called the power function power series (PFPS) is introduced. This new class is obtained by compounding power function and power series distributions. Structural properties...
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Article
Stress–Strength Reliability for the Generalized Inverted Exponential Distribution Using MRSS
In this paper, we study the estimation of the stress–strength reliability model when the stress and the strength variables are modeled by two independent but not identically distributed random variables from t...
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Article
Correction to: Estimation in Constant Stress Partially Accelerated Life Tests for Weibull Distribution Based on Censored Competing Risks Data
In the original publication of the article the ORCID ID of the co-author Amal S. Hassan has been published incorrectly
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Article
Estimation in Constant Stress Partially Accelerated Life Tests for Weibull Distribution Based on Censored Competing Risks Data
This article deals with the constant–stress partially accelerated life test using type I and type II censored data in the presence of competing failure causes. Suppose that the occurrence time of the failure c...
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Article
Power Lindley-G Family of Distributions
In this paper, we introduce a new family of probability distributions generated from a power Lindley random variable called the power Lindley-generated family. The new family extends several classical distribu...
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Article
On the Inverse Power Lomax Distribution
We introduce and study a new three-parameter lifetime distribution named as the inverse power Lomax. The proposed distribution is obtained as the inverse form of the power Lomax distribution. Some statistical ...
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Article
Transmuted Kumaraswamy Quasi Lindley Distribution with Applications
The Lindley distribution is one of the widely used models for studying most of reliability modeling. Besides, several of researchers have motivated new classes of distributions based on modifications of the qu...