线性再生散度模型的极大Lq-似然估计

Maximum Lq-likelihood estimation of reproductive dispersion linear models

  • 摘要: 线性再生散度模型是线性回归模型、广义线性模型和指数线性模型等统计模型的自然推广,极大Lq-似然估计是极大似然估计的推广。本文在Fahrmeir等人的基础上,得到了线性再生散度模型的极大Lq-似然估计的弱相合性、强相合性、渐近正态性,推广了现有的相关结论。最后,给出了一个模拟算例,结果表明:估计方法是有效的;极大 Lq-似然估计具有良好的稳健性。

     

    Abstract: Reproductive dispersion linear models (RDLMS) are the natural generalization of statistical models such as linear regression models, generalized linear models and exponential linear models. Maximum Lq-likelihood estimation (MLqE) is a generalization of maximum likelihood estimation. Based on Fahrmeir et al, the weak consistency, strong consistency and asymptotic normality of the MLqE of RDLMS are obtained, and the existing conclusions are generalized. Finally, a simulation example is given. The results show that our estimation method is effective and the maximum Lq-likelihood estimation is robust.

     

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