InverseGaussian exponential family.
Parameters: | link : a link instance, optional
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Notes
The inverse Guassian distribution is sometimes referred to in the literature as the Wald distribution.
Attributes
InverseGaussian.link | a link instance | The link function of the inverse Gaussian instance |
InverseGaussian.variance | varfunc instance | variance is an instance of statsmodels.family.varfuncs.mu_cubed |
Methods
deviance(endog, mu[, freq_weights, scale]) | Inverse Gaussian deviance function |
fitted(lin_pred) | Fitted values based on linear predictors lin_pred. |
loglike(endog, mu[, freq_weights, scale]) | The log-likelihood function in terms of the fitted mean response. |
predict(mu) | Linear predictors based on given mu values. |
resid_anscombe(endog, mu) | The Anscombe residuals for the inverse Gaussian distribution |
resid_dev(endog, mu[, scale]) | Returns the deviance residuals for the inverse Gaussian family. |
starting_mu(y) | Starting value for mu in the IRLS algorithm. |
variance | |
weights(mu) | Weights for IRLS steps |
Methods
deviance(endog, mu[, freq_weights, scale]) | Inverse Gaussian deviance function |
fitted(lin_pred) | Fitted values based on linear predictors lin_pred. |
loglike(endog, mu[, freq_weights, scale]) | The log-likelihood function in terms of the fitted mean response. |
predict(mu) | Linear predictors based on given mu values. |
resid_anscombe(endog, mu) | The Anscombe residuals for the inverse Gaussian distribution |
resid_dev(endog, mu[, scale]) | Returns the deviance residuals for the inverse Gaussian family. |
starting_mu(y) | Starting value for mu in the IRLS algorithm. |
weights(mu) | Weights for IRLS steps |