一种新的I型半logistic逆威布尔分布及其在镇痛药患者缓解时间数据中的应用

A. Elhassanein
{"title":"一种新的I型半logistic逆威布尔分布及其在镇痛药患者缓解时间数据中的应用","authors":"A. Elhassanein","doi":"10.1166/jmihi.2022.3937","DOIUrl":null,"url":null,"abstract":"This article presents a new extension of the type I half-logistic inverse Weibull distribution. It is used as a base line to construct a new bivariate model that is called bivariate extended type I half-logistic inverse Weibull model. Statistical properties of the proposed distributions\n are derived in explicit forms. Maximum likelihood estimators are discussed. Simulation is employed to discuss theoretical properties, to investigate the performance of the new models and to elaborate the goodness of fit. The new models are applied to real data sets.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Type I Half-Logistic Inverse Weibull Distribution with an Application to the Relief Times Data of Patients Receiving an Analgesic\",\"authors\":\"A. Elhassanein\",\"doi\":\"10.1166/jmihi.2022.3937\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents a new extension of the type I half-logistic inverse Weibull distribution. It is used as a base line to construct a new bivariate model that is called bivariate extended type I half-logistic inverse Weibull model. Statistical properties of the proposed distributions\\n are derived in explicit forms. Maximum likelihood estimators are discussed. Simulation is employed to discuss theoretical properties, to investigate the performance of the new models and to elaborate the goodness of fit. The new models are applied to real data sets.\",\"PeriodicalId\":393031,\"journal\":{\"name\":\"J. Medical Imaging Health Informatics\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Medical Imaging Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1166/jmihi.2022.3937\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Medical Imaging Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1166/jmihi.2022.3937","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

本文给出了I型半逻辑逆威布尔分布的一个新的推广。以此为基础,构造了一个新的二元扩展I型半逻辑逆威布尔模型。所提出的分布的统计性质以显式形式推导出来。讨论了极大似然估计。通过仿真来讨论新模型的理论性质,研究新模型的性能,并阐述拟合优度。将新模型应用于实际数据集。
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
A New Type I Half-Logistic Inverse Weibull Distribution with an Application to the Relief Times Data of Patients Receiving an Analgesic
This article presents a new extension of the type I half-logistic inverse Weibull distribution. It is used as a base line to construct a new bivariate model that is called bivariate extended type I half-logistic inverse Weibull model. Statistical properties of the proposed distributions are derived in explicit forms. Maximum likelihood estimators are discussed. Simulation is employed to discuss theoretical properties, to investigate the performance of the new models and to elaborate the goodness of fit. The new models are applied to real data sets.
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
Application Value of CT Perfusion Imaging in Patients with Posterior Circulation Hyperacute Cerebral Infarction An Operative Acute Brain Tumor Recognition by Jointure Inward Unswerving Probabilistic Neural Network Classifier Making Semi-Automatic Segmentation Method to be Automatic Using Deep Learning for Biventricular Segmentation Improved Wavelet Filter Bank Selection for Effective Feature Extraction in Alzheimer Classification An Efficient Approach to Detect Meningioma Brain Tumor Using Adaptive Neuro Fuzzy Inference System 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