Restricted Estimation of Generalized Linear Models
研究了在参数线性约束下广义线性模型的最大似然估计，提出基于惩罚函数的迭代算法，并讨论了三种检验方法，通过数值示例展示应用，还扩展到岭估计和分段回归。
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.