Minimum contrast estimation in fractional Ornstein-Uhlenbeck process: Continuous and discrete sampling

Author(s):  
Jaya Bishwal

AbstractThe paper shows that the distribution of the normalized minimum contrast estimator of the drift parameter in the fractional Ornstein-Uhlenbeck process observed over [0, T] converges to the standard normal distribution with an uniform error rate of the order O(T −1/2) for the case H > 1/2 where H is the Hurst exponent of the fractional Brownian motion driving the Ornstein-Uhlenbeck process. Then based on discrete observations, it introduces several approximate minimum contrast estimators and studies their rate of of weak convergence to normal distribution.

2019 ◽  
Vol 64 (3) ◽  
pp. 502-525
Author(s):  
Farez Alazemi ◽  
Farez Alazemi ◽  
Soukhana Douissi ◽  
Soukhana Douissi ◽  
Khalifa Es-Sebaiy ◽  
...  

Рассматривается задача оценивания сноса смешанного процесса Орнштейна-Уленбека на основе наблюдений в фиксированные дискретные моменты времени. С использованием исчисления Маллявена и недавнего анализа Нурдина-Пеккати исследуется асимптотическое поведение оценки. Более точно, изучаются сильная состоятельность и асимптотическое распределение оценки; установлена также скорость ее сходимости по распределению для всех $H\in(0,1)$. Более того, доказано, что в случае $H\in(0,3/4]$ оценка удовлетворяет центральной предельной теореме для сходимости почти наверное.


Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2139
Author(s):  
Xiuqiong Chen ◽  
Jiayi Kang ◽  
Mina Teicher ◽  
Stephen S.-T. Yau

Nonlinear filtering is of great significance in industries. In this work, we develop a new linear regression Kalman filter for discrete nonlinear filtering problems. Under the framework of linear regression Kalman filter, the key step is minimizing the Kullback–Leibler divergence between standard normal distribution and its Dirac mixture approximation formed by symmetric samples so that we can obtain a set of samples which can capture the information of reference density. The samples representing the conditional densities evolve in a deterministic way, and therefore we need less samples compared with particle filter, as there is less variance in our method. The numerical results show that the new algorithm is more efficient compared with the widely used extended Kalman filter, unscented Kalman filter and particle filter.


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