Results 231 to 240 of about 13,578,374 (288)
Some of the next articles are maybe not open access.
The least‐squares meshfree method
International Journal for Numerical Methods in Engineering, 2001AbstractA new efficient meshfree method is presented in which the first‐order least‐squares method is employed instead of the Galerkin's method. In the meshfree methods based on the Galerkin formulation, the source of many difficulties is in the numerical integration. The current method, in this respect, has different characteristics and is expected to
Park, SH, Youn, SK Youn, Sung-Kie
openaire +1 more source
An Abbreviation of the Method of Least Squares.
The Journal of Physical Chemistry, 1941Not ...
Cox, G. J., Matuschak, M. C.
openaire +2 more sources
International Journal of Computer Mathematics, 2002 In a recent paper [4], Li et al. gave a generalized successive overrelaxation (GSCR) method for the least squares problems. In this paper, we show that the GSOR method can be applied to the equality constrained least squares (LSE) problems and the ...
Li ZJ(李长军), Evans, DJ
exaly +1 more source
On the Method of Internal Least Squares
Biometrics, 1979Consider a nonlinear relation y(x) = f(x, 0) + e(x) in which y(x) is a random variable observed at an independent scalar mathematical variable x; f is a known function of x; o = (01, 02, ''', 02m+q) iS a vector of unknown parameters, and e(x) is an unobserved random error variable.
openaire +1 more source
IEEE Transactions on Instrumentation and Measurement, 2003
This paper deals with the problem of the regression of measured quantities when measurement uncertainty affects both the regressed quantity and the independent variables. A new criterion is given, named the full least-squares method. A compact matrix notation is used for deriving the parameter vector of the regression model and its uncertainty variance-
openaire +2 more sources
This paper deals with the problem of the regression of measured quantities when measurement uncertainty affects both the regressed quantity and the independent variables. A new criterion is given, named the full least-squares method. A compact matrix notation is used for deriving the parameter vector of the regression model and its uncertainty variance-
openaire +2 more sources
1981
In the next three chapters we shall discuss a particular form of statistical model, which gives rise to simple statistical methods of very wide applicability. The basic model has been mentioned in Section 2.1, Equation (2.2), see also Example 3.12, but the following example illustrates how it arises in practice.
openaire +2 more sources
In the next three chapters we shall discuss a particular form of statistical model, which gives rise to simple statistical methods of very wide applicability. The basic model has been mentioned in Section 2.1, Equation (2.2), see also Example 3.12, but the following example illustrates how it arises in practice.
openaire +2 more sources
Least squares and total least squares methods in image restoration
1997Image restoration is the process of removing or minimizing degradations (blur) in an image. Mathematically, it can be modeled as a discrete ill-posed problem Hf=g, where H is a matrix of large dimension representing the blurring phenomena, and g is a vector representing the observed image.
Julie Kamm, James G. Nagy
openaire +2 more sources
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.
openaire +2 more sources
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.
openaire +2 more sources
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
openaire +2 more sources