混杂正态分布及其在玻璃纤维强度数据中的应用

D. S. Shibu, Soman Latha Nitin, C. Chesneau, M. Irshad, Sobhanam Padmini Shibin, R. Maya
{"title":"混杂正态分布及其在玻璃纤维强度数据中的应用","authors":"D. S. Shibu, Soman Latha Nitin, C. Chesneau, M. Irshad, Sobhanam Padmini Shibin, R. Maya","doi":"10.3390/dynamics2040023","DOIUrl":null,"url":null,"abstract":"The hybrid normal (HN) distribution is a new generalization of the normal distribution that we introduce and study in this article. Its mathematical foundation is based on the logarithmically transformed version of the famous hybrid log-normal (HLN) distribution, which is an unexplored direction in the literature. We emphasize the applicability of the HN distribution with the aim of fitting versatile data, such as, in this paper, fiber data on the strength of glass. In particular, the unknown parameters are estimated using both Bayesian and maximum likelihood estimation approaches, with Bayesian estimation carried out using the MCMC approach. A thorough simulation study is performed to determine the flexibility of the estimates’ performance. The glass fiber data are then analyzed, with an assessment of several existing distributions from the literature used to demonstrate how the HN distribution is relevant in this regard.","PeriodicalId":80276,"journal":{"name":"Dynamics (Pembroke, Ont.)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the Hybrid Normal Distribution and Its Application in Fiber Data on the Strength of Glass\",\"authors\":\"D. S. Shibu, Soman Latha Nitin, C. Chesneau, M. Irshad, Sobhanam Padmini Shibin, R. Maya\",\"doi\":\"10.3390/dynamics2040023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The hybrid normal (HN) distribution is a new generalization of the normal distribution that we introduce and study in this article. Its mathematical foundation is based on the logarithmically transformed version of the famous hybrid log-normal (HLN) distribution, which is an unexplored direction in the literature. We emphasize the applicability of the HN distribution with the aim of fitting versatile data, such as, in this paper, fiber data on the strength of glass. In particular, the unknown parameters are estimated using both Bayesian and maximum likelihood estimation approaches, with Bayesian estimation carried out using the MCMC approach. A thorough simulation study is performed to determine the flexibility of the estimates’ performance. The glass fiber data are then analyzed, with an assessment of several existing distributions from the literature used to demonstrate how the HN distribution is relevant in this regard.\",\"PeriodicalId\":80276,\"journal\":{\"name\":\"Dynamics (Pembroke, Ont.)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Dynamics (Pembroke, Ont.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/dynamics2040023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dynamics (Pembroke, Ont.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/dynamics2040023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

混合正态分布是本文介绍和研究的正态分布的一种新的推广。它的数学基础是基于著名的混合对数正态分布(HLN)的对数变换版本,这是一个文献中尚未探索的方向。我们强调HN分布的适用性,目的是拟合通用数据,例如,在本文中,纤维数据对玻璃的强度。特别是,使用贝叶斯和极大似然估计方法对未知参数进行估计,其中贝叶斯估计使用MCMC方法进行。进行了彻底的模拟研究,以确定估计性能的灵活性。然后对玻璃纤维数据进行分析,并对文献中的几种现有分布进行评估,以证明HN分布在这方面的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
On the Hybrid Normal Distribution and Its Application in Fiber Data on the Strength of Glass
The hybrid normal (HN) distribution is a new generalization of the normal distribution that we introduce and study in this article. Its mathematical foundation is based on the logarithmically transformed version of the famous hybrid log-normal (HLN) distribution, which is an unexplored direction in the literature. We emphasize the applicability of the HN distribution with the aim of fitting versatile data, such as, in this paper, fiber data on the strength of glass. In particular, the unknown parameters are estimated using both Bayesian and maximum likelihood estimation approaches, with Bayesian estimation carried out using the MCMC approach. A thorough simulation study is performed to determine the flexibility of the estimates’ performance. The glass fiber data are then analyzed, with an assessment of several existing distributions from the literature used to demonstrate how the HN distribution is relevant in this regard.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Exploring Transition from Stability to Chaos through Random Matrices Robust Global Trends during Pandemics: Analysing the Interplay of Biological and Social Processes Unveiling Dynamical Symmetries in 2D Chaotic Iterative Maps with Ordinal-Patterns-Based Complexity Quantifiers Thermal Hydraulics Simulation of a Water Spray System for a Cooling Fluid Catalytic Cracking (FCC) Regenerator Investigation of Jamming Phenomenon in a Direct Reduction Furnace Pellet Feed System Using the Discrete Element Method
×
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