{"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}
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.
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.