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].