The accuracy of least squares calculations with the Cholesky algorithm

DOI10.1016/0024-3795(90)90357-IzbMath0696.65100OpenAlexW2067377353WikidataQ126977550 ScholiaQ126977550MaRDI QIDQ911243

A. A. Rayner, J. H. Randall

Publication date: 1990

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0024-3795(90)90357-i

zbMATH Keywords

accuracyscalingleast squares methodill-conditioned linear systemscenteringnormal equationCholesky algorithmcomputer errorsmaximum variance inflation factorpolynomial regression model

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Numerical solutions to overdetermined systems, pseudoinverses (65F20) Probabilistic methods, stochastic differential equations (65C99)

Cites Work

• Collinearity and least squares regression
• Symmetric decomposition of a positive definite matrix
• Programming multiple regression
• Centering and scaling of regression algorithms in the face of iii–conditioning
• Orthogonalization-Triangularization Methods in Statistical Computations
• Modified Square Root Method of Matrix Inversion
• A test of the numerical accuracy of some matrix inversion algorithms commonly used in least squares programs
• Ridge Regression in Practice
• Least Squares Programs-A Look at the Square Root Procedure
• Least squares computations based on the cholesky decomposition of the correlation matrix
• Tolerance and Condition in Regression Computations
• Least squares, singular values and matrix approximations
• Quadratic Forms and Idempotent Matrices with Random Elements
• A Report on the Accuracy of Some Widely Used Least Squares Computer Programs
• Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
• Pitfalls in Computation, or why a Math Book isn't Enough
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