Abstract: In this paper, one defines the generalized linear models(GLM) based on the observed data with incomplete information and random censorship under the case that regressors are given and regressors are stochastic, respectively. Under the given conditions, one discusses the existence and uniqueness of the solution on the likelihood equations with respect to the parameter vector of the two models, obtains and proves the consistency and asymptotical
normality of the maximum likelihood estimators(MLE) on the two models, respectively.

Key words: Generalized linear model, Incomplete information, Consistency, Asymptotical normality