AM  Vol.2 No.3 , March 2011
Iterated Logarithm Laws on GLM Randomly Censored with Random Regressors and Incomplete Information
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.

Cite this paper
nullQ. Zhu, Z. Xiao, G. Qin and F. Ying, "Iterated Logarithm Laws on GLM Randomly Censored with Random Regressors and Incomplete Information," Applied Mathematics, Vol. 2 No. 3, 2011, pp. 363-368. doi: 10.4236/am.2011.23043.
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