Iterated Logarithm Laws on GLM Randomly Censored with Random Regressors and Incomplete Information
Qiang Zhu, Zhihong Xiao, Guanglian Qin, Fang Ying
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DOI: 10.4236/am.2011.23043   PDF    HTML     4,300 Downloads   8,031 Views  

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.

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Q. 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.

Conflicts of Interest

The authors declare no conflicts of interest.

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