LAPACK's user's guide
Block Downdating of Least Squares Solutions
SIAM Journal on Matrix Analysis and Applications
Matrix computations (3rd ed.)
An algorithm to estimate time-varying parameter SURE models under different types of restriction
Computational Statistics & Data Analysis - Special issue: Computational econometrics
Computational methods for modifying seemingly unrelated regressions models
Journal of Computational and Applied Mathematics - Special issue: Proceedings of the international conference on linear algebra and arithmetic, Rabat, Morocco, 28-31 May 2001
Seemingly unrelated regression model with unequal size observations: computational aspects
Computational Statistics & Data Analysis
Pipeline Givens sequences for computing the QR decomposition on a EREW PRAM
Parallel Computing
Efficient algorithms for estimating the general linear model
Parallel Computing - Parallel matrix algorithms and applications (PMAA'04)
Computationally efficient methods for estimating the updated-observations SUR models
Applied Numerical Mathematics
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Five computationally efficient algorithms for block downdating of the least squares solutions are proposed. The algorithms are block versions of Givens rotations strategies and are rich in BLAS-3 operations. They efficiently exploit the triangular structure of the matrices. The theoretical complexities of the algorithms are derived and analyzed. The performance of the implementations confirms the theoretical results. The new strategies are found to outperform existing downdating methods.