Results 271 to 280 of about 4,861,455 (309)

Method of least squares

2009
The method of least squares controls the flow of errors via the elements of the design matrix. Hence, assuming linear systems with differing design matrices aiming at the same set of unknowns, the adjustment’s uncertainties would differ even if the uncertainties of the input data were the same.
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Maximum likelihood, least squares, and penalized least squares for PET

IEEE Transactions on Medical Imaging, 1993
The EM algorithm is the basic approach used to maximize the log likelihood objective function for the reconstruction problem in positron emission tomography (PET). The EM algorithm is a scaled steepest ascent algorithm that elegantly handles the nonnegativity constraints of the problem. It is shown that the same scaled steepest descent algorithm can be
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Least Squares or Least Circles?

CHANCE, 2010
(2010). Least Squares or Least Circles? CHANCE: Vol. 23, Collecting Data in Challenging Settings, pp. 38-42.
Ivo Petras, Igor Podlubny
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Least Squares Sign-Solvability

SIAM Journal on Matrix Analysis and Applications, 1995
Let $Ax = b$ be a linear system. We study the relationship between the sign pattern of the least squares solution to $Ax = b$ and the sign patterns of A and b. The system $Ax = b$ is least squares sign-solvable if the signs of the entries in its least square solution can be determined solely from the signs of the entries of A and b.
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Review of Ordinary Least Squares and Generalized Least Squares

1984
The purpose of this chapter is to review the fundamentals of ordinary least squares and generalized least squares in the context of linear regression analysis. The presentation here is somewhat condensed given our objective of focusing on more advanced topics in econometrics.
Thomas B. Fomby, Stanley R. Johnson, R. Carter Hill   +2 more
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Least squares parameter estimation

Automatica, 1979
Abstract This article demonstrates the application of least squares for the estimation of system parameters. Analytic as well as numerical approaches are described. The model of the system dynamics is assumed in the form of regression model. Solutions are discussed for the case of white noise and correlated noise corrupting the useful output signal ...
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An Algorithm for Least Squares

Journal of Mathematics and Physics, 1947
Nielsen, K. L., Goldstein, L.
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Least Squares Parameter Estimation and Multi-Innovation Least Squares Methods for Linear Fitting Problems From Noisy Data

Journal of Computational and Applied Mathematics, 2023
Feng Ding
exaly  

Least Median of Squares Regression

Journal of the American Statistical Association, 1984
Peter Rousseeuw
exaly   +2 more sources

Generalized Least Squares

Technometrics, 2005
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