报告题目：Maximum Likelihood Estimation for Symmetricα-stable Ornstein-Unlenbeck Processes

主 讲 人：陈晓鹏

单 位：汕头大学

时 间：11月24日20:00

腾 讯 ID：990 580 430

摘 要：

We consider the maximum likelihood estimation for the symmetric α-stable Ornstein-Unlenbeck (SαS-OU ) processes based on discrete observations. Since the closed-form expression of maximum likelihood function is hard to obtain in the Lévy case, we choose a mixture of Cauchy and Gaussian distribution to approximate the probability density function (PDF) of the SαS distribution. By means of transition function and Laplace transform, we construct an explicit approximate sequence of likelihood function, which converges to the likelihood function of SαS distribution. Based on the approximation of likelihood function we give an algorithm for computing maximum likelihood estimation. We also numerically simulate some experiments which demonstrate the accuracy and stability of the proposed estimator.

简 介：

陈晓鹏，汕头大学副教授。2010年博士毕业于华中科技大学，2010年至2014年先后在澳大利亚阿德雷德大学和北京大学北京国际数学研究中心做博士后工作。主持完成国家自然科学青年基金，广东省自然科学基金，中国博士后科学基金，论文发表在Proc. Amer. Math. Soc.，Dyn. Sys.，P. Roy. Soc. Edinb. A.等国际数学期刊上。