is called the weighted

least-squares estimate of [beta].

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.

This problem, which we call the linearized

least-squares problem for rational interpolation, is the starting point of the algorithm we recommend in this article, and we describe the mathematical basis of how we solve it in the next section.

Recent work on Shepard methods have focused on reducing the computational cost by limiting the

least-squares fitting process to a local subset of the data.

Next, we first transform the

least-squares problem with respect to the matrix equation (4.

Almost half a century ago, Henri Theil introduced symmetric price and quantity index numbers based on an elegant

least-squares principle.

Keywords: meshless methods, moving

least-squares, weight function.

The uncertainties identified by item (a) contribute to the deviations in the responses from the

least-squares fit to the data and are accounted for by the uncertainty [u.

When the model is correct and the observations are affected only by random errors then the classic

least-squares estimation yields the most likely solution.

Regression "instruments" and the regression procedure known as two-stage

least-squares are typically used to estimate a demand equation.