Comparison of PLS algorithms when number of objects is much larger than number of variables

DOI10.1007/s00362-009-0251-7zbMath1247.62163OpenAlexW2076603440MaRDI QIDQ451312

Aylin Alin

Publication date: 23 September 2012

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-009-0251-7

zbMATH Keywords

partial least squareshigh-dimensional datamultiple linear regressionmulticollinearitykernel matrixNIPALSSIMPLS

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Point estimation (62F10) Complexity and performance of numerical algorithms (65Y20)

Related Items (2)

A kernel PLS based classification method with missing data handling ⋮ PLS for Big Data: a unified parallel algorithm for regularised group PLS

Cites Work

• Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy
• Cross-Validation of Regression Models
• Linear Model Selection by Cross-Validation
• Introduction to the bootstrap world

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