Results 11 to 20 of about 2,031,080 (289)
Nonlinear least squares method
Communications, 1999 The paper deals with a comparison of linear and nonlinear least squares approximation. Its aim is to show that the well known transformations of nonlinear dependencies on linear dependencies do not always give exact results.
Jaromír Máca, Bohus Leitner
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Revstat Statistical Journal, 2015 Regression procedures are often used for estimating distributional parameters because of their computational simplicity and useful graphical presentation. However, the resulting regression model may have heteroscedasticity and/or correction problems and
Yeliz Mert Kantar
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Penalized partial least squares for pleiotropy
BMC Bioinformatics, 2021 Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects.
Camilo Broc +2 more
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Ciencias Marinas, 2002 It is desired to represent, as good as possible, a series of data by means of certain functions with free parameters. "As good as possible" means that these parameters ara chosen so that the residuals, the difference between data and fitting functions,
P Ripa
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Robust linear least squares regression [PDF]
, 2011 We consider the problem of robustly predicting as well as the best linear combination of $d$ given functions in least squares regression, and variants of this problem including constraints on the parameters of the linear combination.
Audibert, Jean-Yves, Catoni, Olivier
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Unifying Least Squares, Total Least Squares and Data Least Squares [PDF]
, 2002 The standard approaches to solving overdetermined linear systems A x ≈ b construct minimal corrections to the vector b and/or the matrix A such that the corrected system is compatible. In ordinary least squares (LS) the correction is restricted to b, while in data least squares (DLS) it is restricted to A. In scaled total least squares (Scaled TLS) [15]
Christopher C. Paige, Zdeněk Strakoš
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Least squares auto-tuning [PDF]
Engineering Optimization, 2020 Least squares is by far the simplest and most commonly applied computational method in many fields. In almost all applications, the least squares objective is rarely the true objective. We account for this discrepancy by parametrizing the least squares problem and automatically adjusting these parameters using an optimization algorithm.
Shane T. Barratt, Stephen P. Boyd
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Total Least Squares Spline Approximation
Mathematics, 2019 Spline approximation, using both values y i and x i as observations, is of vital importance for engineering geodesy, e.g., for approximation of profiles measured with terrestrial laser scanners, because it enables the consideration of
Frank Neitzel +2 more
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Geoscience Letters, 2022 The linear relationship between two stable water isotopes (δD and δ18O) has been used to examine the physical processes and movements or changes of three water phases (water vapor, liquid water and ice), including deuterium excess.
Jeonghoon Lee +3 more
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Götterdämmerung over total least squares
Journal of Geodetic Science, 2016 The traditional way of solving non-linear least squares (LS) problems in Geodesy includes a linearization of the functional model and iterative solution of a nonlinear equation system.
Malissiovas G., Neitzel F., Petrovic S.
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