使用广义双曲分布的基于模型的聚类、分类和判别分析:MixGHD R包

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Statistical Software Pub Date : 2021-01-01 DOI:10.18637/jss.v098.i03
C. Tortora, R. Browne, Aisha Elsherbiny, B. Franczak, P. McNicholas
{"title":"使用广义双曲分布的基于模型的聚类、分类和判别分析:MixGHD R包","authors":"C. Tortora, R. Browne, Aisha Elsherbiny, B. Franczak, P. McNicholas","doi":"10.18637/jss.v098.i03","DOIUrl":null,"url":null,"abstract":"The MixGHD package for R performs model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution (GHD). This approach is suitable for data that can be considered a realization of a (multivariate) continuous random variable. The GHD has the advantage of being flexible due to skewness, concentration, and index parameters; as such, clustering methods that use this distribution are capable of estimating clusters characterized by different shapes. The package provides five different models all based on the GHD, an efficient routine for discriminant analysis, and a function to measure cluster agreement. This paper is split into three parts: the first is devoted to the formulation of each method, extending them for classification and discriminant analysis applications, the second focuses on the algorithms, and the third shows the use of the package on real datasets.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package\",\"authors\":\"C. Tortora, R. Browne, Aisha Elsherbiny, B. Franczak, P. McNicholas\",\"doi\":\"10.18637/jss.v098.i03\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The MixGHD package for R performs model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution (GHD). This approach is suitable for data that can be considered a realization of a (multivariate) continuous random variable. The GHD has the advantage of being flexible due to skewness, concentration, and index parameters; as such, clustering methods that use this distribution are capable of estimating clusters characterized by different shapes. The package provides five different models all based on the GHD, an efficient routine for discriminant analysis, and a function to measure cluster agreement. This paper is split into three parts: the first is devoted to the formulation of each method, extending them for classification and discriminant analysis applications, the second focuses on the algorithms, and the third shows the use of the package on real datasets.\",\"PeriodicalId\":17237,\"journal\":{\"name\":\"Journal of Statistical Software\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Software\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.18637/jss.v098.i03\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Software","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.18637/jss.v098.i03","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 11

摘要

用于R的MixGHD包使用广义双曲分布(GHD)执行基于模型的聚类、分类和判别分析。这种方法适用于可以被认为是一个(多变量)连续随机变量的实现的数据。GHD的优点是由于偏度、浓度和指标参数而具有灵活性;因此,使用这种分布的聚类方法能够估计具有不同形状特征的聚类。该包提供了五个不同的模型都基于GHD,一个有效的例程判别分析,和一个功能来衡量聚类协议。本文分为三部分:第一部分致力于每种方法的制定,将其扩展到分类和判别分析应用;第二部分侧重于算法;第三部分展示了该软件包在实际数据集上的使用。
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
Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package
The MixGHD package for R performs model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution (GHD). This approach is suitable for data that can be considered a realization of a (multivariate) continuous random variable. The GHD has the advantage of being flexible due to skewness, concentration, and index parameters; as such, clustering methods that use this distribution are capable of estimating clusters characterized by different shapes. The package provides five different models all based on the GHD, an efficient routine for discriminant analysis, and a function to measure cluster agreement. This paper is split into three parts: the first is devoted to the formulation of each method, extending them for classification and discriminant analysis applications, the second focuses on the algorithms, and the third shows the use of the package on real datasets.
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
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
期刊最新文献
spsurvey: Spatial Sampling Design and Analysis in R. Application of Equal Local Levels to Improve Q-Q Plot Testing Bands with R Package qqconf. Elastic Net Regularization Paths for All Generalized Linear Models. Broken Stick Model for Irregular Longitudinal Data jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets
×
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