scholarly journals Sharp Large Deviations for the Ornstein - Uhlenbeck Process

2001 ◽  
Vol 46 (1) ◽  
pp. 74-93 ◽  
Author(s):  
Bernard Bercu ◽  
Bernard Bercu ◽  
Alain Rouault ◽  
Alain Rouault
Keyword(s):  
Large Deviations ◽  
Uhlenbeck Process ◽  
Ornstein Uhlenbeck Process ◽  
Sharp Large Deviations

scholarly journals Sharp large deviations for the fractional Ornstein - Uhlenbeck process

2010 ◽  
Vol 55 (4) ◽  
pp. 732-771 ◽  
Author(s):  
Bernard Bercu ◽  
Bernard Bercu ◽  
Laure Coutin ◽  
Laure Coutin ◽  
Nicolas Savy ◽  
...  
Keyword(s):  
Large Deviations ◽  
Uhlenbeck Process ◽  
Ornstein Uhlenbeck Process ◽  
Sharp Large Deviations

Self-normalized asymptotic properties for the parameter estimation in fractional Ornstein–Uhlenbeck process

2019 ◽  
Vol 19 (03) ◽  
pp. 1950018 ◽  
Author(s):  
Hui Jiang ◽  
Junfeng Liu ◽  
Shaochen Wang
Keyword(s):  
Large Deviations ◽  
Asymptotic Properties ◽  
Moderate Deviations ◽  
Delta Method ◽  
Fourth Moment ◽  
Test Statistics ◽  
Uhlenbeck Process ◽  
Ornstein Uhlenbeck Process ◽  
Deviation Inequalities ◽  
Type Ii Errors

In this paper, we consider the self-normalized asymptotic properties of the parameter estimators in the fractional Ornstein–Uhlenbeck process. The deviation inequalities, Cramér-type moderate deviations and Berry–Esseen bounds are obtained. The main methods include the deviation inequalities and moderate deviations for multiple Wiener–Itô integrals [P. Major, Tail behavior of multiple integrals and U-statistics, Probab. Surv. 2 (2005) 448–505; On a multivariate version of Bernsteins inequality, Electron. J. Probab. 12 (2007) 966–988; M. Schulte and C. Thäle, Cumulants on Wiener chaos: Moderate deviations and the fourth moment theorem, J. Funct. Anal. 270 (2016) 2223–2248], as well as the Delta methods in large deviations [F. Q. Gao and X. Q. Zhao, Delta method in large deviations and moderate deviations for estimators, Ann. Statist. 39 (2011) 1211–1240]. For applications, we propose two test statistics which can be used to construct confidence intervals and rejection regions in the hypothesis testing for the drift coefficient. It is shown that the Type II errors tend to be zero exponential when using the proposed test statistics.

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