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 language | English |
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Pages (from-to) | 201-206 |
Number of pages | 6 |
Journal | Biometrics |
Volume | 46 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1990 |