标题:
非线性联合均值方差模型的统计分析Statistical Analysis for Nonlinear Joint Mean and Variance Models
作者:
周梦齐, 徐登可, 杨佳红, 王梦滢
关键字:
非线性联合均值方差模型, 异方差, Gauss-Newton, 极大似然估计Nonlinear Joint Mean and Variance Models, Heteroscedasticity, Gauss-Newton, Maximum Likelihood Estimate
期刊名称:
《Statistics and Application》, Vol.3 No.2, 2014-06-10
摘要:
在提出非线性均值方差模型的基础上，研究了该模型中未知参数的估计问题。主要是基于Gauss-Newton迭代算法给出该模型中未知参数的极大似然估计。通过大量随机模拟实验验证了所提出方法的有效性。最后，结合实际问题数据验证了该模型与方法具有实用性和可行性。
We propose nonlinear joint mean and variance models in this paper and investigate the estimate for unknown parameters in the model based on Gauss-Newton iterative algorithm. Furthermore, we make some simulations to show that the proposed procedure works satisfactorily. Lastly, two real examples are presented to illustrate the proposed methodology.