What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation

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What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation

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Title: What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation
Author: Gill, Jeff; King, Gary

Note: Order does not necessarily reflect citation order of authors.

Citation: Gill, Jeff, and Gary King. 2004. What to do when your Hessian is not invertible: Alternatives to model respecification in nonlinear estimation. Sociological Methods and Research 33(1): 54-87.
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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.
Published Version: doi:10.1177/0049124103262681
Other Sources: http://smr.sagepub.com/cgi/content/abstract/33/1/54
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4214881

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  • FAS Scholarly Articles [7470]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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