What to Do When Your Hessian Is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation
Jeff Gill, Gary King. 2004.
"What to Do When Your Hessian Is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation".
Sociological Methods & Research, 33, 1, Pp. 54–87.

Abstract
What should a researcher do when statistical analysis software terminates before completion with a message that the Hessian is not invertable? The standard textbook advice is to respecify the model, but this is another way of saying that the researcher should change the question being asked. Obviously, however, computer programs should not be in the business of deciding what questions are worthy of study. Although noninvertable Hessians are sometimes signals of poorly posed questions, nonsensical models, or inappropriate estimators, they also frequently occur when information about the quantities of interest exists in the data, through the likelihood function. We explain the problem in some detail and lay out two preliminary proposals for ways of dealing with noninvertable Hessians without changing the question asked.
See Also
- [Dataset] Replication data (Harvard Dataverse)
- [Paper] How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science (1986)
- [Book] Numerical Issues Involved in Inverting Hessian Matrices (2003)
- [Paper] On Political Methodology (1991)
- [Book] The Changing Evidence Base of Social Science Research (2009)
- [Paper] If a Statistical Model Predicts That Common Events Should Occur Only Once in 10,000 Elections, Maybe It's the Wrong Model (2025)
- [Paper] The Significance of Roll Calls in Voting Bodies: A Model and Statistical Estimation (1986)