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has been cited by the following article:

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

  AUTHORS: Qiang Zhu, Zhihong Xiao, Guanglian Qin, Fang Ying

  KEYWORDS: Generalized Linear Model, Incomplete Information, Stochastic Regressor, Iterated Logarithm Laws

  JOURNAL NAME: Applied Mathematics, Vol.2 No.3, March 24, 2011

  ABSTRACT: 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.