Generalized Pareto Model: Properties and Applications in Neutrosophic Data Modeling

Abstract

The Pareto distribution is widely used to model industrial, biological, engineering, and other various types of data. A new generalized model, namely the neutrosophic Pareto distribution (NPD), is developed in this article. The proposed model is a neutrosophic variant of the classical Pareto distribution, potentially useful for analyzing vague, unclear, indeterminate, or imprecise data. The structure form of the proposed distribution is skewed to the right and determined to be unimodal. Several characteristics of the NPD are investigated under the neutrosophic framework. The expressions for basic properties such as mean, variance, raw moments, and shape coefficients are obtained. The maximum likelihood approach is presented for estimating the imprecise distributional parameters of the proposed model. The extended notions of the NPD are explained with various key functions in the domain of applied statistical methods. Finally, the practical benefits of NPD are proven by analyzing two real datasets.

Kumaraswamy Sine Inverted Rayleigh distribution : Properties and Application to Bladder Cancer Data

“…[14] explores the mathematical properties of the Sin-G class of distributions, focusing on the special member SinIW, investigates its theoretical and practical aspects, and demonstrates its utility through an application to real-life data. [13] introduced a weighted cosine-G family to analyze time-to-event data. [12] expanded on this family by proposing extended cosine-G distributions with application for lifetime studies.…”

A new one-parameter discrete probability distribution with its neutrosophic extension: mathematical properties and applications

“…The concept of neutrosophic probability as a function was originally presented by [ 32 ], where U is a neutrosophic sample space and defined the probability mapping to take the form where and . Furthermore, many scholars have studied various neutrosophic probability models such as Poisson, binomial, exponential, uniform, normal, Weibull, Kumaraswamy, generalized Pareto, Maxwell, Lognormal, and Gamma, see [ 2 , 9 , 11 , 23 – 25 , 29 , 31 ]. In many cases, researchers investigate goodness-of-fit tests, neutrosophic time series prediction, and modeling, such as neutrosophic logarithmic models, neutrosophic moving averages, and neutrosophic linear models, as shown in [ 3 , 10 , 13 ].…”