A Review of Envelope Models. (27th January 2020)

• Record Type: Journal Article
• Authors: Lee, Minji; Su, Zhihua
• Is Part Of: International statistical review. Volume 88:Number 3(2020)
• Page Start: 658
• Page End: 676
• Publication Date: 2020-01-27
• Subjects: dimension reduction -- envelope model -- generalised linear models -- multivariate linear regression -- partial least squares; Statistics -- Periodicals; Statistics -- Bibliography -- Periodicals; Statistics -- Bibliography; Statistics -- Periodicals; Statistique; Statistique -- Périodiques; Statistique -- Bibliographie -- Périodiques; Statistique -- Étude et enseignement -- Périodiques; Statistique -- Étude et enseignement -- Bibliographie -- Périodiques; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 519.2
• DOI: 10.1111/insr.12361
• Languages: English
• ISSNs: 0306-7734

Abstract

Summary: The envelope model was first introduced as a parsimonious version of multivariate linear regression. It uses dimension reduction techniques to remove immaterial variation in the data and has the potential to gain efficiency in estimation and improve prediction. Many advances have taken place since its introduction, and the envelope model has been applied to many contexts in multivariate analysis, including partial least squares, generalised linear models, Bayesian analysis, variable selection and quantile regression, among others. This article serves as a review of the envelope model and its developments for those who are new to the area.