On the fitting of generalized linear models with nonnegativity parameter constraints

J. W. McDonald, I. D. Diamond

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

We consider the problem of finding maximum likelihood estimates of a generalized linear model when some or all of the regression parameters are constrained to be nonnegative. The Kuhn-Tucker conditions of nonlinear programming can be used to characterize the solution of this constrained estimation problem when the maximum likelihood estimates exist and are unique. For the case of a generalized linear model with nonnegativity parameter constraints, the Kuhn-Tucker conditions are derived and utilized to provide a stopping rule for search algorithms for the constrained maximum likelihood estimates. Two examples are discussed.

Original languageEnglish
Pages (from-to)201-206
Number of pages6
JournalBiometrics
Volume46
Issue number1
DOIs
Publication statusPublished - 1990

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