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statsmodels.regression.linear_model.WLS.fit

WLS.fit(method='pinv', **kwargs)

Full fit of the model.

The results include an estimate of covariance matrix, (whitened) residuals and an estimate of scale.

Parameters :

method : str

Can be “pinv”, “qr”, or “mle”. “pinv” uses the Moore-Penrose pseudoinverse to solve the least squares problem. “svd” uses the Singular Value Decomposition. “qr” uses the QR factorization. “mle” fits the model via maximum likelihood. “mle” is not yet implemented.

Returns :

A RegressionResults class instance. :

See also

regression.RegressionResults

Notes

Currently it is assumed that all models will have an intercept / constant in the design matrix for postestimation statistics.

The fit method uses the pseudoinverse of the design/exogenous variables to solve the least squares minimization.

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