Efficient Maximum Likelihood Estimator for the Drift Parameter of a Generalized Langevin Equation (01-Oct-20)

 WEBINAR

Efficient Maximum Likelihood Estimator for the Drift Parameter of a Generalized Langevin Equation

Felipe Sousa Quintino (MAT/UnB)

In this webinar, we will present a maximum likelihood estimator (MLE) for the Generalized Ornstein-Uhlenbeck process of the Fluctuating Exponential type driven by a Lévy process. This process was introduced by de Alcântara (2019). He showed that this process is a new class of solution to the Generalized Langevin Equation (GLE). We derived a general form to the MLE for the drift parameter of a GLE. Our strategy is based on Mai (2012, 2014) and Gloter et al. (2018). We will discuss these results and how they are supported by ergodicity, the law of larges numbers and central limit theorem for martingales, LAN property and efficiency in the sense of Hájek-LeCam convolution theorem.

01-Oct-2020 (Thursday), 2:00 pm (Brasília time)