Large-Sample Theory for Generalized Linear Models with Non-natural Link and Random Variates

Abstract

For generalized linear models (GLM), in the case that the regressors are stochastic and have different distributions and the observations of the responses may have different dimensionality, the asymptotic theory of the maximum likelihood estimate (MLE) of the parameters are studied under the assumption of a non-natural link function.