A comparison of algorithms for forming the QR decomposition for use in regression

DOI10.1016/0167-9473(92)90176-GzbMath0875.62304OpenAlexW1971699106MaRDI QIDQ2563596

Yanyan Li

Publication date: 10 November 1997

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

Full work available at URL: https://doi.org/10.1016/0167-9473(92)90176-g

zbMATH Keywords

Givens rotationsHouseholder transformationsGram-Schmidt methodsforward stepwise regression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Probabilistic methods, stochastic differential equations (65C99)

Related Items (1)

Iterative refinement of the singular-value decomposition solution to the regression equations

Uses Software

• AS 79
• LINPACK
• AS 164
• AS 46

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Benchmarking numerical accuracy of statistical algorithms
• Numerical methods for solving linear least squares problems
• Modified gram-schmidt process vs. classical gram-schmidt
• Algorithm AS 164: Least Squares Subject to Linear Constraints
• The Acceptability of Regression Solutions: Another Look at Computational Accuracy
• Reorthogonalization and Stable Algorithms for Updating the Gram-Schmidt QR Factorization
• Numerically Stable Methods for Updating Regressions
• Least Squares Computations by Givens Transformations Without Square Roots
• A Note on Implementing the Householder Transformation
• Experiments on Error Growth Associated with Some Linear Least-Squares Procedures
• A Report on the Accuracy of Some Widely Used Least Squares Computer Programs

This page was built for publication: A comparison of algorithms for forming the QR decomposition for use in regression