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GNU Octave Manual Version 3
by John W. Eaton, David Bateman, Søren Hauberg
Paperback (6"x9"), 568 pages
ISBN 095461206X
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23.4 Linear Least Squares

Octave also supports linear least squares minimization. That is, Octave can find the parameter b such that the model y = x*b

fits data (x,y) as well as possible, assuming zero-mean Gaussian noise. If the noise is assumed to be isotropic the problem can be solved using the ‘\’ or ‘/’ operators, or the ols function. In the general case where the noise is assumed to be anisotropic the gls is needed.

Function File: [beta, sigma, r] = ols (y, x)
Ordinary least squares estimation for the multivariate model y = x b + e with mean (e) = 0 and cov (vec (e)) = kron (s, I).

where y is a t by p matrix, x is a t by k matrix, b is a k by p matrix, and e is a t by p matrix.

Each row of y and x is an observation and each column a variable.

The return values beta, sigma, and r are defined as follows.

beta
The OLS estimator for b, beta = pinv (x) * y, where pinv (x) denotes the pseudoinverse of x.
sigma
The OLS estimator for the matrix s,
sigma = (y-x*beta)'
  * (y-x*beta)
  / (t-rank(x))
r
The matrix of OLS residuals, r = y - x * beta.

Function File: [beta, v, r] = gls (y, x, o)
Generalized least squares estimation for the multivariate model y = x b + e with mean (e) = 0 and cov (vec (e)) = (s^2) o,

where y is a t by p matrix, x is a t by k matrix, b is a k by p matrix, e is a t by p matrix, and o is a t p by t p matrix.

Each row of y and x is an observation and each column a variable. The return values beta, v, and r are defined as follows.

beta
The GLS estimator for b.
v
The GLS estimator for s^2.
r
The matrix of GLS residuals, r = y - x beta.
ISBN 095461206XGNU Octave Manual Version 3See the print edition