Generalized orthogonal components regression for high dimensional generalized linear models

DOI10.1016/j.csda.2015.02.006zbMath1468.62123arXiv1304.4890OpenAlexW2075043630WikidataQ58170695 ScholiaQ58170695MaRDI QIDQ1663286

Dabao Zhang, Min Zhang, Yanzhu Lin

Publication date: 21 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1304.4890

zbMATH Keywords

classificationcategorical datacollineardimension reduction multicollinear

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Generalized linear models (logistic models) (62J12)

Cites Work

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• Penalized orthogonal-components regression for large \(p\) small \(n\) data
• Sparse partial least squares classification for high dimensional data
• Handbook of partial least squares. Concepts, methods and applications.
• On the existence of maximum likelihood estimates in logistic regression models
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Iteratively Reweighted Partial Least Squares Estimation for Generalized Linear Regression
• Bias reduction of maximum likelihood estimates
• An invariant form for the prior probability in estimation problems

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