scholarly journals Parameter estimation for the discretely observed fractional Ornstein–Uhlenbeck process and the Yuima R package

2012 ◽  
Vol 28 (4) ◽  
pp. 1529-1547 ◽  
Author(s):  
Alexandre Brouste ◽  
Stefano M. Iacus
Keyword(s):  
Parameter Estimation ◽  
R Package ◽  
Uhlenbeck Process ◽  
Ornstein Uhlenbeck Process

scholarly journals Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process

2021 ◽  
Author(s):  
Antoine Bichat ◽  
Christophe Ambroise ◽  
Mahendra Mariadassou
Keyword(s):  
Multiple Testing ◽  
R Package ◽  
Statistical Testing ◽  
Uhlenbeck Process ◽  
Explanatory Variables ◽  
New Associations ◽  
Penalized Estimation ◽  
Ultrametric Tree ◽  
Ornstein Uhlenbeck Process ◽  
The Mean

AbstractStatistical testing is classically used as an exploratory tool to search for association between a phenotype and many possible explanatory variables. This approach often leads to multiple testing under dependence. We assume a hierarchical structure between tests via an Ornstein-Uhlenbeck process on a tree. The process correlation structure is used for smoothing the p-values. We design a penalized estimation of the mean of the Ornstein-Uhlenbeck process for p-value computation. The performances of the algorithm are assessed via simulations. Its ability to discover new associations is demonstrated on a metagenomic dataset. The corresponding R package is available from https://github.com/abichat/zazou.

scholarly journals Berry–Esséen bound for the parameter estimation of fractional Ornstein–Uhlenbeck processes

2019 ◽  
Vol 20 (04) ◽  
pp. 2050023 ◽  
Author(s):  
Yong Chen ◽  
Nenghui Kuang ◽  
Ying Li
Keyword(s):  
Parameter Estimation ◽  
Brownian Motion ◽  
Continuous Time ◽  
Index Formula ◽  
Hurst Index ◽  
Uhlenbeck Process ◽  
Ornstein Uhlenbeck Process ◽  
Time Observation ◽  
Statistical Estimations ◽  
Drift Parameter

For an Ornstein–Uhlenbeck process driven by fractional Brownian motion with Hurst index [Formula: see text], we show the Berry–Esséen bound of the least squares estimator of the drift parameter based on the continuous-time observation. We use an approach based on Malliavin calculus given by Kim and Park [Optimal Berry–Esséen bound for statistical estimations and its application to SPDE, J. Multivariate Anal. 155 (2017) 284–304].

Modeling Copper Prices: Ornstein-Uhlenbeck Process and Jump-Diffusions

2012 ◽  
Vol 461 ◽  
pp. 793-796
Author(s):  
Xi Bing Li ◽  
Yu Xi Hu ◽  
Zhen Zhong Zhang ◽  
Xin Ru Liu
Keyword(s):  
Parameter Estimation ◽  
Moment Method ◽  
Futures Market ◽  
Uhlenbeck Process ◽  
Jump Diffusions ◽  
Ornstein Uhlenbeck Process ◽  
Futures Price ◽  
Generalized Moment

In this paper we focus on parameter estimation of the futures price processes with a Ornstein-Uhlenbeck process and jump-diffusions. We use the generalized moment method to derive the OU process. Afterwards, we fit a jump diffusions model to Copper prices from Shanghai Copper futures market.

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