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Laws of iterated logarithm for MLE of generalized linear model randomly censored with incomplete information

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  • Xiao, Zhihong
  • Liu, Luqin

Abstract

In this paper, we define the generalized linear model (GLM) based on the observed data with incomplete information in the case of random censorship, and obtain a law of iterated logarithm and a Chung type law of iterated logarithm for the maximum likelihood estimator (MLE) in this model.

Suggested Citation

  • Xiao, Zhihong & Liu, Luqin, 2009. "Laws of iterated logarithm for MLE of generalized linear model randomly censored with incomplete information," Statistics & Probability Letters, Elsevier, vol. 79(6), pages 789-796, March.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:6:p:789-796
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