Results 211 to 220 of about 3,165,100 (264)
Evaluating permutation-based inference for partial least squares analysis of neuroimaging data. [PDF]
Imaging Neurosci (Camb)Danyluik M +8 more
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Experimental validation of the precision of sinusoidal amplitude estimation using a least squares procedure in the presence of additive noise. [PDF]
Sci RepBarzegar M, Alegria F.
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Disentangling the Impacts of PAHs, Microplastics, and Sediment Resuspension on Algal Physiology: A Partial Least Squares Structural Equation Modeling Approach. [PDF]
ACS Environ AuLo HS +5 more
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Overview of total least-squares methods
Signal Processing, 2007 We review the development and extensions of the classical total least squares method and describe algorithms for its generalization to weighted and structured approximation problems.
Sabine Van Huffel, Ivan Markovsky
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Least squares estimates and the coverage of least squares costs
52nd IEEE Conference on Decision and Control, 2013The least squares estimate xN minimizes the sum of the squared residuals equation over a finite set of observations (Ai, bi). At x = xN, the squared residuals ∥AixN-bi∥2 are called the “empirical costs”. Intuitively, the empirical costs carry information on the probability distribution of the cost ∥AxN-b∥2 that is paid for other, yet unseen, values of (
Carè, Algo +2 more
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Proceedings Shape Modeling Applications, 2004., 2004
In this paper we introduce least-squares meshes: meshes with a prescribed connectivity that approximate a set of control points in a least-squares sense. The given mesh consists of a planar graph with arbitrary connectivity and a sparse set of control points with geometry. The geometry of the mesh is reconstructed by solving a sparse linear system. The
Olga Sorkine, Daniel Cohen-Or
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In this paper we introduce least-squares meshes: meshes with a prescribed connectivity that approximate a set of control points in a least-squares sense. The given mesh consists of a planar graph with arbitrary connectivity and a sparse set of control points with geometry. The geometry of the mesh is reconstructed by solving a sparse linear system. The
Olga Sorkine, Daniel Cohen-Or
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Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression
2012Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table.
Hervé, Abdi, Lynne J, Williams
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The Inefficiency of Least Squares
Biometrika, 1975SUMMARY Two criteria are set up to judge the relative performance of the least squares estimator and the best linear unbiased estimator of , in the linear model y = X/, + u, where E(u) = 0, E(uu') = F. The matrices X and r are found so that the relative performance of least squares is worst.
Bloomfield, Peter, Watson, Geoffrey S.
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2016
The English version of this paper appeared two years after the Chinese “original”. During the 1950s and early 1960s, DDK visited China several times on exchange programmes.
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The English version of this paper appeared two years after the Chinese “original”. During the 1950s and early 1960s, DDK visited China several times on exchange programmes.
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Information Sciences, 1988
The author discusses three models of fuzzy linear regression function for triangular fuzzy numbers [Fuzzy numbers with triangular shapes, cf. \textit{D. Dubois} and \textit{H. Prade}, Int. J. Syst. Sci. 9, 613-626 (1978; Zbl 0383.94045)]. Formulas are deduced by the least-squares method.
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The author discusses three models of fuzzy linear regression function for triangular fuzzy numbers [Fuzzy numbers with triangular shapes, cf. \textit{D. Dubois} and \textit{H. Prade}, Int. J. Syst. Sci. 9, 613-626 (1978; Zbl 0383.94045)]. Formulas are deduced by the least-squares method.
openaire +3 more sources