For symmetric problems, at least one of the subproblems is a nonlinear

least squares problem, e.

Modified Gram Schmidt orthogonalization approach for computing the

least squares residual error (MGS-LSRE)

The QR decomposition is used to solve equation (1) such that we can find the

least squares solution [?

For validation of the

least squares yield estimation model, two additional sets of cutting bills were created: (1) five cutting bills using the Buehlmann cutting bill but uniform random part quantities (Table 1, columns 6 through 10 [labeled 1 through 5]) and (2) five cutting bills from the literature and industry that were clustered to fit the Buehlmann cutting bill (see Buehlmann 1998:213-215, appendices G and H).

Ordinary

least squares estimation and robust MM-estimation represent parametric hedonic methods, structural time series estimation is a semiparametric hedonic method and robust local regression is a nonparametric hedonic method.

The Total

Least Squares (TLS) principle, as developed in Golub and Van Loan (1980), is an elegant method to treat problems where all the data are affected by random errors.

We determine the model parameters by the method of ordinary

least squares and their associated 1-sigma random uncertainties by a nonparametric bootstrap resampling scheme [17-19].

He compares the results using ordinary

least squares (OLS) with three alternative methods of estimation.

17) Table 3 lists the instruments used here to predict the wireless price in the two-stage

least squares regression.

The product is based on recently developed

Least Squares Monte Carlo methodology.

Ten reprinted papers deal with singular value decomposition (SVD), QR-decomposition (QRD), and recursive

least squares (RLS) minimization with different algorithms and architectures.

A robust regression [12] of age at death on quarter of death found an increase of 20 days per year, nearly identical to the 26 days found by ordinary

least squares regression.