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

dc.creator	Cirillo, Marcelo Angelo
dc.creator	Barroso, Lúcia Pereira
dc.date.accessioned	2020-11-09T20:23:36Z
dc.date.available	2020-11-09T20:23:36Z
dc.date.issued	2012-05
dc.description.abstract	The 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.	pt_BR
dc.identifier.citation	CIRILLO, 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.	pt_BR
dc.identifier.uri	https://repositorio.ufla.br/handle/1/45432
dc.identifier.uri	https://digitalcommons.wayne.edu/jmasm/vol11/iss1/4/	pt_BR
dc.language	en_US	pt_BR
dc.publisher	Wayne State University	pt_BR
dc.rights	openAccess	pt_BR
dc.source	Journal of Modern Applied Statistical Methods	pt_BR
dc.subject	Accuracy	pt_BR
dc.subject	Monte Carlo simulation	pt_BR
dc.subject	Normal asymmetry	pt_BR
dc.subject	Precision	pt_BR
dc.subject	Métodos de regressão	pt_BR
dc.subject	Modelagem de equações estruturais	pt_BR
dc.subject	Simulação de Monte Carlo	pt_BR
dc.title	Robust regression estimates in the prediction of latent variables in structural equation models	pt_BR
dc.type	Artigo	pt_BR

Arquivos

Agora exibindo 1 - 1 de 1