多变量广义因子的解析闭式解

Q3 Mathematics
S. Lipovetsky, Vladimir Manewitsch
{"title":"多变量广义因子的解析闭式解","authors":"S. Lipovetsky, Vladimir Manewitsch","doi":"10.22237/jmasm/1556668980","DOIUrl":null,"url":null,"abstract":"The factor analytic triad method of one-factor solution gives the explicit analytical form for a common latent factor built by three variables. The current work considers analytical presentation of a general latent factor constructed in a closed-form solution for multivariate case. The results can be supportive to theoretical description and practical application of latent variable modeling, especially for big data because the analytical closed-form solution is not prone to data dimensionality.","PeriodicalId":47201,"journal":{"name":"Journal of Modern Applied Statistical Methods","volume":"18 1","pages":"2-23"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Analytical Closed-Form Solution for General Factor with Many Variables\",\"authors\":\"S. Lipovetsky, Vladimir Manewitsch\",\"doi\":\"10.22237/jmasm/1556668980\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The factor analytic triad method of one-factor solution gives the explicit analytical form for a common latent factor built by three variables. The current work considers analytical presentation of a general latent factor constructed in a closed-form solution for multivariate case. The results can be supportive to theoretical description and practical application of latent variable modeling, especially for big data because the analytical closed-form solution is not prone to data dimensionality.\",\"PeriodicalId\":47201,\"journal\":{\"name\":\"Journal of Modern Applied Statistical Methods\",\"volume\":\"18 1\",\"pages\":\"2-23\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Modern Applied Statistical Methods\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22237/jmasm/1556668980\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Modern Applied Statistical Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22237/jmasm/1556668980","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 1

摘要

单因子解的因子分析三元法给出了由三个变量构成的共同潜因子的显式分析形式。当前的工作考虑了在多变量情况的闭式解中构造的一般潜在因子的分析表示。研究结果可以支持潜在变量建模的理论描述和实际应用,特别是对于大数据,因为分析闭合形式的解不倾向于数据维度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analytical Closed-Form Solution for General Factor with Many Variables
The factor analytic triad method of one-factor solution gives the explicit analytical form for a common latent factor built by three variables. The current work considers analytical presentation of a general latent factor constructed in a closed-form solution for multivariate case. The results can be supportive to theoretical description and practical application of latent variable modeling, especially for big data because the analytical closed-form solution is not prone to data dimensionality.
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
CiteScore
0.50
自引率
0.00%
发文量
5
期刊介绍: The Journal of Modern Applied Statistical Methods is an independent, peer-reviewed, open access journal designed to provide an outlet for the scholarly works of applied nonparametric or parametric statisticians, data analysts, researchers, classical or modern psychometricians, and quantitative or qualitative methodologists/evaluators.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信