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  • References
  • Python
  • Statsmodels
  • Linear Regression

WLS.loglike()

statsmodels.regression.linear_model.WLS.loglike

WLS.loglike(params) [source]

Returns the value of the gaussian log-likelihood function at params.

Given the whitened design matrix, the log-likelihood is evaluated at the parameter vector params for the dependent variable Y.

Parameters:

params : array-like

The parameter estimates.

Returns:

llf : float

The value of the log-likelihood function for a WLS Model.

Notes

-\frac{n}{2}\log\left(Y-\hat{Y}\right)-\frac{n}{2}\left(1+\log\left(\frac{2\pi}{n}\right)\right)-\frac{1}{2}log\left(\left|W\right|\right)

where W is a diagonal matrix

Links:
  • http://statsmodels.sourceforge.net/stable/generated/statsmodels.regression.linear_model.WLS.loglike.html
doc_statsmodels
doc_statsmodels
2025-01-10 15:47:30
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