1 (Partial Column-wise Least-Squares
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.
combination of terrestrial and satellite data in physical geodesy, Ann.
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
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
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
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].