Results 41 to 50 of about 9,204,806 (93)

On the computation of the structured total least squares estimator

open access: yes, 2004
A class of structured total least squares problems is considered, in which the extended data matrix is partitioned into blocks and each of the blocks is (block) Toeplitz/Hankel structured, unstructured, or noise free.
Van Huffel, S.   +2 more
core   +1 more source

On the equivalence between Total Least Squares and Maximum Likelihood PCA

open access: yes, 2005
The maximum likelihood PCA (MLPCA) method has been devised in chemometrics as a generalization of the well-known PCA method in order to derive consistent estimators in the presence of errors with known error distribution.
Wentzell, P.   +7 more
core   +1 more source

Least-squares methods for identifying biochemical regulatory networks from noisy measurements [PDF]

open access: yes, 2007
<b>Background</b>: We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data.
Postlethwaite, I.   +23 more
core   +1 more source

On the complex least squares problem with constrained phase [PDF]

open access: yes, 2011
The problem of solving approximately in the least squares sense an overdetermined linear system of equations with complex valued coefficients is considered, where the elements of the solution vector are constrained to have the same phase.
Markovsky, Ivan
core   +2 more sources

An adapted version of the element-wise weighted total least squares method for applications in chemometrics

open access: yes, 2007
The Maximum Likelihood PCA (MLPCA) method has been devised in chemometrics as a generalization of the well-known PCA method in order to derive consistent estimators in the presence of errors with known error distribution.
Markovsky, Ivan   +2 more
core   +1 more source

Consistency of the structured total least squares estimator in a multivariate errors-in-variables model

open access: yes, 2005
The structured total least squares estimator, defined via a constrained optimization problem, is a generalization of the total least squares estimator when the data matrix and the applied correction satisfy given structural constraints.
Van Huffel, S.   +2 more
core   +1 more source

NARX-based nonlinear system identification using orthogonal least squares basis hunting [PDF]

open access: yes, 2008
An orthogonal least squares technique for basis hunting (OLS-BH) is proposed to construct sparse radial basis function (RBF) models for NARX-type nonlinear systems.
Wang, X.X.   +2 more
core   +1 more source

Algorithms and literate programs for weighted low-rank approximation with missing data

open access: yes, 2010
Linear models identification from data with missing values is posed as a weighted low-rank approximation problem with weights related to the missing values equal to zero.
Markovsky, Ivan, Ivan Markovsky
core   +1 more source

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