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.

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

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

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.

nullQ. Zhu, Z. Xiao, G. Qin and F. Ying, "Iterated Logarithm Laws on GLM Randomly Censored with Random Regressors and Incomplete Information,"

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