Iterated Logarithm Laws on GLM Randomly Censored with Random Regressors and Incomplete Information  

作　　者：Qiang Zhu Zhihong Xiao Guanglian Qin Fang Ying 

机构地区：[1]不详

出　　处：《Applied Mathematics》2011年第3期363-368,共6页应用数学（英文）

摘　　要：In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, we obtain a law of iterated logarithm and a Chung type law of iterated logarithm for the maximum likelihood estimator (MLE) in the present model.In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, we obtain a law of iterated logarithm and a Chung type law of iterated logarithm for the maximum likelihood estimator (MLE) in the present model.

关 键 词：Generalized Linear Model INCOMPLETE Information Stochastic Regressor ITERATED LOGARITHM LAWS 

分 类 号：O21[理学—概率论与数理统计]