Partial Least Squares Regression for Binary Responses and its associated Biplot Representation
L. Vicente González, J. L. Vicente-Villardon
In this paper we develop a generalization of Partial Least Squares Regression (PLSR) to cope with a set of binary responses and a matrix of numerical predictors. We call the method Partial Least Squares Binary Logistic Regression (PLS-BLR). Biplot representations for visualization of both PLS and PLS-BLR models are described and an application to real data is presented. Software packages for the calculation of the main results are also provided. We conclude that proposed method and its visualization using biplots, are powerful tools for the interpretation of the relations among predictors and responses.
Keywords: binary data, PLS, PLS-BLR, biplot
Scheduled
GT04 Multivariate Analysis and Classification II
June 7, 2022 3:30 PM
Cloister room
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M. Comas-Cufí, J. A. Martín-Fernández, G. Mateu-Figueras, J. Palarea-Albaladejo
S. Tarazona Campos, M. C. Nieto Romero
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