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.
ASJC Scopus subject areas
- Statistics and Probability
- Immunology and Microbiology(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
- Applied Mathematics