{"title":"An Innovative Technique for Generating Probability Distributions: A Study on Lomax Distribution with Applications in Medical and Engineering Fields","authors":"Shamshad Ur Rasool, M. A. Lone, S. P. Ahmad","doi":"10.1007/s40745-024-00515-6","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"12 2","pages":"439 - 455"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Data Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s40745-024-00515-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
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.
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.