least squares

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least squares

(lēst skwārz),
A principle of estimation invented by Gauss in which the estimates of a set of parameters in a statistical model are the quantities that minimize the sum of squared differences between the observed values of the dependent variable and the values predicted by the model.

least squares

a method of regression analysis. The line on a graph that best summarizes the relationship between two variables is the one that ensures that there is the least value of the sum of the squares of the deviation between the fitted curve and each of the original data points.
References in periodicals archive ?
1 (Partial Column-wise Least-Squares Method (PCLS)).
The basic problem used in this paper is the non-negative least-squares (NNLS) problem, minimizing [[parallel]Ex - b[parallel].
In the presence of such heterogeneity, conventional least-squares regression models may underestimate, overestimate, or fail to detect important changes occurring locally at a certain quanfile of data, because it focuses on changes in the means (Terrell et al.
Borin A, Ferrao MF, Mello C, Maretto DA, Poppi RJ (2006) Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk.
Least-squares combination of terrestrial and satellite data in physical geodesy, Ann.
The least-squares solution: The vector of estimated parameters that solves the LS problem is:
If linear least-squares computations are performed for the data for the multimeters shown here, the standard deviation of the individual data points about the fitted line may be calculated for each multimeter.
1995) (describing two-stage least-squares estimation process).
The authors of the article correctly mention that the least-squares equation will be centered on the two means in a bivariate equation and will be rotated by outliers.
With this constraint, he derived equations for the slope and intercept for a weighted least-squares regression model.
In my empirical work below I use two sets of estimates of expected inflation--one based on a survey and one based on least-squares learning.
The algebraic distance measure we propose is based on a notion of strength of connectivity among variables that is derived from the local least-squares (LS) formulation for computing caliber-one interpolation in the BAMG process [3, 4, 24].

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