Statistical Hybridization of Normal and Weibull Distributions with its Properties and Applications

Oyetunde Aa
{"title":"Statistical Hybridization of Normal and Weibull Distributions with its Properties and Applications","authors":"Oyetunde Aa","doi":"10.4172/2168-9679.1000424","DOIUrl":null,"url":null,"abstract":"The normal distribution is one of the most popular probability distributions with applications to real life data. In this research paper, an extension of this distribution together with Weibull distribution called the Weimal distribution which is believed to provide greater flexibility to model scenarios involving skewed data was proposed. The probability density function and cumulative distribution function of the new distribution can be represented as a linear combination of exponential normal density functions. Analytical expressions for some mathematical quantities comprising of moments, moment generating function, characteristic function and order statistics were presented. The estimation of the proposed distribution’s parameters was undertaken using the method of maximum likelihood estimation. Two data sets were used for illustration and performance evaluation of the proposed model. The results of the comparative analysis to other baseline models show that the proposed distribution would be more appropriate when dealing with skewed data.","PeriodicalId":15007,"journal":{"name":"Journal of Applied and Computational Mathematics","volume":"39 1","pages":"62-68"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied and Computational Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4172/2168-9679.1000424","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The normal distribution is one of the most popular probability distributions with applications to real life data. In this research paper, an extension of this distribution together with Weibull distribution called the Weimal distribution which is believed to provide greater flexibility to model scenarios involving skewed data was proposed. The probability density function and cumulative distribution function of the new distribution can be represented as a linear combination of exponential normal density functions. Analytical expressions for some mathematical quantities comprising of moments, moment generating function, characteristic function and order statistics were presented. The estimation of the proposed distribution’s parameters was undertaken using the method of maximum likelihood estimation. Two data sets were used for illustration and performance evaluation of the proposed model. The results of the comparative analysis to other baseline models show that the proposed distribution would be more appropriate when dealing with skewed data.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
正态分布和威布尔分布的统计杂交及其性质和应用
正态分布是应用于实际生活数据的最流行的概率分布之一。在本研究中,提出了该分布与威布尔分布的扩展,称为威姆分布,该分布被认为可以为涉及偏斜数据的场景建模提供更大的灵活性。新分布的概率密度函数和累积分布函数可以表示为指数正态密度函数的线性组合。给出了由矩、矩生成函数、特征函数和阶统计量组成的数学量的解析表达式。采用极大似然估计方法对所提出的分布参数进行估计。使用两个数据集对所提出的模型进行了说明和性能评估。与其他基线模型的对比分析结果表明,所提出的分布在处理偏态数据时更为合适。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Energy is Not Conserved Algorithm for Solving Multi-Delay Optimal Control Problems Using Modified Alternating Direction Method of Multipliers Numerical Approach for Determining Impact of Steric Effects in Biological Ion Channel Assessing the Impact of Age andndash;Vaccination Structure Models on the Dynamics of Tuberculosis Transmission A New One-Dimensional Finite Volume Method for Hyperbolic Conservation Laws
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1