is an option for generalized linear model fitting functions that specifies the estimator for the parameter covariance matrix.



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Basic Examples  (1)

Fit a generalized linear model:

Compute the covariance matrix using the expected information matrix:

Use the observed information matrix instead:

Scope  (2)

Specify the covariance estimate within the FittedModel:

Use with LogitModelFit:

Use with ProbitModelFit:

Properties & Relations  (2)

Error estimates and confidence intervals involve covariance estimates:

Estimate errors and intervals using expected information:

Use observed information:

CovarianceEstimatorFunction controls the general structure of the covariance:

DispersionEstimatorFunction affects the scale:

The ratio of the errors squared is the ratio of the dispersion estimates:

Introduced in 2008