Some parsimonious models for lifetimes and applications

Kavya, P.; Manoharan, M.

doi:10.1080/00949655.2021.1946064

Cited by 23 publications

(18 citation statements)

References 18 publications

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“…Kavya and Manoharan [49] just presented a novel transformation, the KM transformation class of statistical models. The cdf and pdf are provided in the next two equations…”

Section: Introductionmentioning

confidence: 99%

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

Eldessouky

Hassan

Elgarhy

et al. 2023

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Statistical models are useful in explaining and forecasting real-world occurrences. Various extended distributions have been widely employed for modeling data in a variety of fields throughout the last few decades. In this article we introduce a new extension of the Kumaraswamy exponential (KE) model called the Kavya–Manoharan KE (KMKE) distribution. Some statistical and computational features of the KMKE distribution including the quantile (QUA) function, moments (MOms), incomplete MOms (INMOms), conditional MOms (COMOms) and MOm generating functions are computed. Classical maximum likelihood and Bayesian estimation approaches are employed to estimate the parameters of the KMKE model. The simulation experiment examines the accuracy of the model parameters by employing Bayesian and maximum likelihood estimation methods. We utilize two real datasets related to food chain data in this work to demonstrate the importance and flexibility of the proposed model. The new KMKE proposed distribution is very flexible, more so than numerous well-known distributions.

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“…Kavya and Manoharan [49] just presented a novel transformation, the KM transformation class of statistical models. The cdf and pdf are provided in the next two equations…”

Section: Introductionmentioning

confidence: 99%

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

Eldessouky

Hassan

Elgarhy

et al. 2023

Axioms

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“…Still in the spirit of the DUS transformation, Ref. [16] proposed a new class of parsimonious distributions, known as the Kavya-Manoharan (KM) transformation, with the following CDF and PDF:…”

Section: Introductionmentioning

confidence: 99%

Sampling Plan for the Kavya–Manoharan Generalized Inverted Kumaraswamy Distribution with Statistical Inference and Applications

Alsadat

Hassan

Elgarhy

et al. 2023

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In this article, we introduce the Kavya–Manoharan generalized inverse Kumaraswamy (KM-GIKw) distribution, which can be presented as an improved version of the generalized inverse Kumaraswamy distribution with three parameters. It contains numerous referenced lifetime distributions of the literature and a large panel of new ones. Among the essential features and attributes covered in our research are quantiles, moments, and information measures. In particular, various entropy measures (Rényi, Tsallis, etc.) are derived and discussed numerically. The adaptability of the KM-GIKw distribution in terms of the shapes of the probability density and hazard rate functions demonstrates how well it is able to fit different types of data. Based on it, an acceptance sampling plan is created when the life test is truncated at a predefined time. More precisely, the truncation time is intended to represent the median of the KM-GIKw distribution with preset factors. In a separate part, the focus is put on the inference of the KM-GIKw distribution. The related parameters are estimated using the Bayesian, maximum likelihood, and maximum product of spacings methods. For the Bayesian method, both symmetric and asymmetric loss functions are employed. To examine the behaviors of various estimates based on criterion measurements, a Monte Carlo simulation research is carried out. Finally, with the aim of demonstrating the applicability of our findings, three real datasets are used. The results show that the KM-GIKw distribution offers superior fits when compared to other well-known distributions.

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“…Recently, Ref. [ 41 ] introduced a new transformation, the KM transformation family of distributions. The CDF and PDF are, respectively,

and

The hazard rate function (HRF) is provided via

Using a given baseline distribution, this family generates new lifetime models or distributions.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [ 41 ] used the exponential and Weibull distributions as baseline distributions because they are widely used in reliability theory and survival analysis.…”

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

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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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Some parsimonious models for lifetimes and applications

Cited by 23 publications

References 18 publications

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

Sampling Plan for the Kavya–Manoharan Generalized Inverted Kumaraswamy Distribution with Statistical Inference and Applications

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

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