Journal of Guangxi Normal University(Natural Science Edition) ›› 2020, Vol. 38 ›› Issue (3): 59-69.doi: 10.16088/j.issn.1001-6600.2020.03.008

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Law of Iterated Logarithm and Strong Consistency for Negative Binomial Regression Model

YANG Xiaowei1,2, ZHANG Junjian1*   

  1. 1. College of Mathematics and Statistics, Guangxi Normal University, Guilin Guangxi 541006, China;
    2. College of Mathematics and Statistics, Chaohu University, Hefei Anhui 238000, China
  • Received:2019-03-03 Online:2020-05-25 Published:2020-06-11

Abstract: The structure of negative binomial regression model selection is constructed, and the asymptotic properties of logarithmic likelihood function and its dependence on relation function in model selection are studied. The law of iterated logarithm for maximum likelihood estimation of model parameters is derived, and the strong consistency criterion for selecting such models is established. It is proved that under some general conditions, if the order of penalty terms increases with the dimension of the model and is between O(lnln n) and O(n), the model selection criterion consisting of negative maximum logarithmic likelihood and penalty terms almost necessarily chooses the simplest correct model.

Key words: law of iterated logarithm, negative binomial regression, maximum likelihood estimation, model selection, strong consistency

CLC Number: 

  • O212.4
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