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Generalized linear model parameter estimation problem

Author: LiFangMing
Tutor: ZhangBaoXue
School: Northeast Normal University
Course: Probability Theory and Mathematical Statistics
Keywords: MLE Random effects MCEM MCNR SML Hierarchical generalized linear models h- likelihood
CLC: O212
Type: Master's thesis
Year: 2007
Downloads: 264
Quote: 0
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Abstract


Generalized linear models in both theory and practical application is very important , in this regard theoretical results are widely used in biology, medicine and other fields. So the problem of generalized linear models is very important , and in the study of such problems parameter estimation has been a focus of everyone's attention one of the problems . Parameter estimation in the model for statistical inference is vital aspects, but the issue multiple numerical integration has brought great difficulties presented difficulties can avoid numerical integration parameter estimation method will become one of the research focus . This paper discusses the generalized linear models often use some of the parameter estimation methods were discussed without random effects generalized linear model and generalized linear mixed model parameter estimation problem, synthesis and evaluation of these methods is given , and through simulation these methods were compared , the simulation results show that the algorithm MCEM algorithm and MCNR Goodness.

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CLC: > Mathematical sciences and chemical > Mathematics > Probability Theory and Mathematical Statistics > Mathematical Statistics
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