An Innovative Technique for Generating Probability Distributions: A Study on Lomax Distribution with Applications in Medical and Engineering Fields

Q1 Decision Sciences
Shamshad Ur Rasool, M. A. Lone, S. P. Ahmad
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引用次数: 0

Abstract

In this paper, we propose and investigate a novel approach for generating the probability distributions. The novel method is known as the SMP transformation technique. By using the SMP Transformation technique, we have developed a new model of the Lomax distribution known as SMP Lomax (SMPL) distribution. The SMPL distribution, which is comparable to the Sine Power Lomax distribution, Power Length BiasedWeighted Lomax Distribution, Exponentiated Lomax and Lomax distribution have the desirable attribute of allowing the superiority and the flexibility over other well known existing models. Furthermore, the research article examines various aspects related to the SMPL , including the statistical properties along with the maximum likelihood estimation procedure to estimate the parameters. An extensive simulation study is carried out to illustrate the behaviour of MLEs on the basis of Mean Square Errors. To evaluate the effectiveness and flexibility of the proposed distribution, two real-life data sets are employed and it is observed that SMPL outperforms base model of Lomax distribution as well as other mentioned competing models based on Akaike Information Criterion, Akaike Information criterion Corrected, Hannan–Quinn information criterion and other goodness of fit measures.

生成概率分布的创新技术:洛马克斯分布在医学和工程领域的应用研究
在本文中,我们提出并研究了一种生成概率分布的新方法。这种新方法被称为SMP转换技术。通过使用SMP变换技术,我们开发了一种新的Lomax分布模型,称为SMP Lomax (SMPL)分布。SMPL分布可与正弦功率Lomax分布、功率长度偏加权Lomax分布、幂次Lomax分布和Lomax分布相媲美,具有比其他已知的现有模型更优越和更灵活的理想属性。此外,本文还研究了与SMPL相关的各个方面,包括统计性质以及估计参数的最大似然估计程序。在均方误差的基础上,进行了广泛的仿真研究,以说明mle的行为。为了评价所提出的分布的有效性和灵活性,采用了两个实际数据集,观察到SMPL优于Lomax分布的基本模型以及基于Akaike信息准则、Akaike信息准则Corrected、Hannan-Quinn信息准则和其他拟合优度度量的其他竞争模型。
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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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