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

Qiang Zhu, Zhihong Xiao, Guanglian Qin, Fang Ying
DOI: 10.4236/am.2011.23043                

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.

Keywords

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

Conflicts of Interest

The authors declare no conflicts of interest.

References

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