Robust regression estimates in the prediction of latent variables in structural equation models

dc.creatorCirillo, Marcelo Angelo
dc.creatorBarroso, Lúcia Pereira
dc.date2020-11-09T20:23:36Z
dc.date2020-11-09T20:23:36Z
dc.date2012-05
dc.date.accessioned2023-09-28T20:08:46Z
dc.date.available2023-09-28T20:08:46Z
dc.identifierCIRILLO, M. A.; BARROSO, L. P. Robust regression estimates in the prediction of latent variables in structural equation models. Journal of Modern Applied Statistical Methods, [S.l.], v. 11, n. 1, p. 42-53, May 2012. DOI: 10.22237/jmasm/1335844980.
dc.identifierhttps://digitalcommons.wayne.edu/jmasm/vol11/iss1/4/
dc.identifierhttp://repositorio.ufla.br/jspui/handle/1/45432
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9045563
dc.descriptionThe incorporation of the robust regression methods Least Median Square (LMS) and Least Trimmed Squares (LTS) is proposed in structural equation modeling. Results show that, in situations of high deviations of symmetry, the evaluated methods would be recommended for applications including smaller sample sizes.
dc.languageen_US
dc.publisherWayne State University
dc.rightsrestrictAccess
dc.sourceJournal of Modern Applied Statistical Methods
dc.subjectAccuracy
dc.subjectMonte Carlo simulation
dc.subjectNormal asymmetry
dc.subjectPrecision
dc.subjectMétodos de regressão
dc.subjectModelagem de equações estruturais
dc.subjectSimulação de Monte Carlo
dc.titleRobust regression estimates in the prediction of latent variables in structural equation models
dc.typeArtigo


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