On a Generalized Lifetime Model Using DUS Transformation

Kavya, P.; Manoharan, M.

doi:10.1007/978-981-15-5951-8_17

Cited by 11 publications

(7 citation statements)

References 9 publications

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“…From Eq. ( 4), the DUS-neutrosophic Weibull becomes the DUS-Weibull model when I N 0 and it is a special case of a generalized DUS-exponential model studied by Kavya and Manoharan [17].…”

Section: Dus-neutrosophic Weibullmentioning

confidence: 99%

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A new neutrosophic model using DUS-Weibull transformation with application

Nayana

Anakha

Chacko

et al. 2022

Complex Intell. Syst.

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There is a need to comprehend real-world problems that are marked by ambiguity and inflexibility. By taking into account the indeterminacies and inconsistencies, DUS transformation has been taken to Neutrosophic Weibull distribution and DUS-Neutrosophic Weibull distribution is proposed. The probability density function is unimodal and decreasing in nature. Several statistical properties have been studied. The parameters of the proposed distribution are estimated using the maximum likelihood method. The proposed distribution has been validated on a real data set. The estimates are found to be more accurate than the classical distributions.

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“…From Eq. ( 4), the DUS-neutrosophic Weibull becomes the DUS-Weibull model when I N 0 and it is a special case of a generalized DUS-exponential model studied by Kavya and Manoharan [17].…”

Section: Dus-neutrosophic Weibullmentioning

confidence: 99%

“…A DUS transformation method has been used to analyze patient information on bladder cancer by Kumar et al [19]. Kavya and Manoharan [17] introduced the Generalized DUS (GDUS) transformation of Weibull distribution. Deepthi and Chacko [10] studied the properties of DUS-Lomax distribution for survival data analysis.…”

Section: Introductionmentioning

confidence: 99%

A new neutrosophic model using DUS-Weibull transformation with application

Nayana

Anakha

Chacko

et al. 2022

Complex Intell. Syst.

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show abstract

“…Deepthi and Chacko [15] proposed the DUS-Lomax distribution, considering the Lomax distribution as the baseline distribution for the DUS transformation. Kavya and Manoharan [16] obtained the DUS-Weibull distribution with the same approach. Maurya et al [17] proposed a generalization of the DUS transformation and studied the exponential baseline in the proposed method.…”

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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“…Ref. [ 40 ] proposed a generalized lifetime model based on the DUS transformation, with the CDF of the GDUS transformation given by

where

The associated density function (PDF) is given by:

where

is the baseline distribution in the

family distribution. This approach will always create a parsimonious distribution because it is a transformation rather than a generalization, so that no additional parameters beyond those in the baseline distribution are introduced.…”

Section: Introductionmentioning

confidence: 99%

Inference for a Kavya–Manoharan Inverse Length Biased Exponential Distribution under Progressive-Stress Model Based on Progressive Type-II Censoring

Alotaibi

Hashem

Elbatal

et al. 2022

Entropy

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In this article, a new one parameter survival model is proposed using the Kavya–Manoharan (KM) transformation family and the inverse length biased exponential (ILBE) distribution. Statistical properties are obtained: quantiles, moments, incomplete moments and moment generating function. Different types of entropies such as Rényi entropy, Tsallis entropy, Havrda and Charvat entropy and Arimoto entropy are computed. Different measures of extropy such as extropy, cumulative residual extropy and the negative cumulative residual extropy are computed. When the lifetime of the item under use is assumed to follow the Kavya–Manoharan inverse length biased exponential (KMILBE) distribution, the progressive-stress accelerated life tests are considered. Some estimating approaches, such as the maximum likelihood, maximum product of spacing, least squares, and weighted least square estimations, are taken into account while using progressive type-II censoring. Furthermore, interval estimation is accomplished by determining the parameters’ approximate confidence intervals. The performance of the estimation approaches is investigated using Monte Carlo simulation. The relevance and flexibility of the model are demonstrated using two real datasets. The distribution is very flexible, and it outperforms many known distributions such as the inverse length biased, the inverse Lindley model, the Lindley, the inverse exponential, the sine inverse exponential and the sine inverse Rayleigh model.

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On a Generalized Lifetime Model Using DUS Transformation

Cited by 11 publications

References 9 publications

A new neutrosophic model using DUS-Weibull transformation with application

A new neutrosophic model using DUS-Weibull transformation with application

An Extension of the UEHL Distribution Based on the DUS Transformation

Inference for a Kavya–Manoharan Inverse Length Biased Exponential Distribution under Progressive-Stress Model Based on Progressive Type-II Censoring

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