当前位置: X-MOL 学术Math. Meth. Stat. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Moment convergence in regularized estimation under multiple and mixed-rates asymptotics
Mathematical Methods of Statistics Pub Date : 2017-07-01 , DOI: 10.3103/s1066530717020016
H. Masuda , Y. Shimizu

In M-estimation under standard asymptotics, the weak convergence combined with the polynomial type large deviation estimate of the associated statistical random field Yoshida (2011) provides us with not only the asymptotic distribution of the associated M-estimator but also the convergence of its moments, the latter playing an important role in theoretical statistics. In this paper, we study the above program for statistical random fields of multiple and also possibly mixedrates type in the sense of Radchenko (2008) where the associated statistical random fields may be nondifferentiable and may fail to be locally asymptotically quadratic. Consequently, a very strong mode of convergence of a wide range of regularized M-estimators is ensured.Our results are applied to regularized estimation of an ergodic diffusion observed at high frequency.

中文翻译:

多重和混合速率渐近性下正则估计中的矩收敛

在标准渐近线下的M估计中,弱收敛与相关统计随机字段的多项式大偏差估计相结合Yoshida(2011)为我们提供了相关M估计的渐近分布,以及其矩的收敛,后者在理论统计中起着重要作用。在本文中,我们从Radchenko(2008)的意义上研究了用于多个或可能是混合速率类型的统计随机场的程序,其中相关的统计随机场可能是不可微的,并且可能无法局部渐近地二次。因此,广泛正则化M的非常强的收敛模式我们的结果被应用于对高频观测到的遍历扩散的正规估计。
更新日期:2017-07-01
down
wechat
bug