Partial least squares prediction in high-dimensional regression

DOI10.1214/18-AOS1681zbMath1416.62389MaRDI QIDQ1731062

Liliana Forzani, R. Dennis Cook

Publication date: 6 March 2019

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aos/1547197242

zbMATH Keywords

predictiondimension reductionsparse regressionabundant regressions

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items

Sparse functional partial least squares regression with a locally sparse slope function, Fast Bayesian inversion for high dimensional inverse problems, Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions, A Review of Envelope Models, Partial least squares for simultaneous reduction of response and predictor vectors in regression, Bootstrapping some GLM and survival regression variable selection estimators, Envelopes and principal component regression, Partial least squares prediction in high-dimensional regression, Comparing six shrinkage estimators with large sample theory and asymptotically optimal prediction intervals, A slice of multivariate dimension reduction, Bootstrapping multiple linear regression after variable selection, Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors

Cites Work

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