Restricted Estimation of Generalized Linear Models

Hans Nyquist

Journal of the Royal Statistical Society Series C, 1991, vol. 40, issue 1, 133-141

Abstract: Maximum likelihood estimation of the generalized linear model under linear restrictions on the parameters is considered. Using a penalty function approach an iterative procedure for obtaining the estimates is proposed. The likelihood ratio test, the Wald test and the Lagrange multiplier test are considered as alternatives for testing a hypothesis about linear restrictions on the parameters. An application of the estimator and the tests is illustrated in a numerical example. The approach extends to a definition of a ridge estimator for generalized linear models and to a definition of piecewise regressions, including cubic spline functions and a nonparametric smoother.

Date: 1991
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