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 ?
Lawson and Hanson (1995) proved that the problem (11) is equivalent to the least-squares problem
If the primal problem (19) has an unbounded optimal solution, then the least-squares problem (23) has for every [z.
If the LP problem is degenerate, ill-conditioned or we have any initial basis, then it is expedient to start the solution process with the least-squares problem (23).
The user has the option of solving systems of nonlinear equations or nonlinear least-squares problems.
First, we tested the software package on the set of nonlinear equations and nonlinear least-squares problems of More et al.
The dimensions of the least-squares problems are m = n = 65536 and the constraints are l = [(0, .
2] as bound-constrained linear least-squares problems by ADM.
Following [19], we now describe a method for computing a solution to the least-squares problem (2.
We now have a true least-squares problem, which again can be solved with the SVD.
Also, rank revealing factorization can be used to solve least-squares problems using the method proposed by Bjorck [1, 2].
1) This is a formal derivation and it is worth noting that one should not form generalized inverses as a method for computing solutions to linear least-squares problems.
The efficient algorithms developed in this work were used to solve large-scale least-squares problems involving millions of observations from the National Geodetic Survey.