Applications to Bio-Medical data and statistical inference for a Kavya-Manoharan log-logistic model

Al-Nefaie, Abdullah H.

doi:10.1016/j.jrras.2023.100523

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…In regard to biomedical data, Ref. [19] presented an extended version of the loglogistic distribution using this transformation. A new expanded form of the Kumaraswamy (Kw) distribution, still based on the KM distribution, was presented in Ref.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

“…Using the transformation Z = δV, where V has the CDF in Equation ( 6) and let ϑ 2 = ϑ 3 = 1, then Z has the KM-Log logistic distribution with parameters (δ, ϑ 1 ) (see Ref. [19]). Hence, the CDF of Z has the following structure:…”

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confidence: 99%