statsmodels.regression.linear_model.WLS.loglike¶
- WLS.loglike(params)[source]¶
Compute 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:¶
- paramsarray_like
The parameter estimates.
- Returns:¶
float
The value of the log-likelihood function for a WLS Model.
Notes
\[-\frac{n}{2}\log SSR -\frac{n}{2}\left(1+\log\left(\frac{2\pi}{n}\right)\right) +\frac{1}{2}\log\left(\left|W\right|\right)\]where \(W\) is a diagonal weight matrix matrix, \(\left|W\right|\) is its determinant, and \(SSR=\left(Y-\hat{Y}\right)^\prime W \left(Y-\hat{Y}\right)\) is the sum of the squared weighted residuals.
Last update:
Oct 12, 2024