Consistency and asymptotic normality of maximum likelihood estimation for Gaussian Markov processes from discrete observations

Consistency and asymptotic normality of maximum likelihood estimation for Gaussian Markov processes from discrete observations

0.00 Avg rating—0 Votes
Article ID: iaor19961853
Country: Germany
Volume: 43
Issue: 1
Start Page Number: 69
End Page Number: 90
Publication Date: Jan 1996
Journal: Metrika
Authors:
Abstract:

This paper proves the weak consistency and the asymptotic normality of the maximum likelihood estimation based on discrete observations of n independent Gaussian Markov processes. The Ornstein Uhlenbeck process is a special Gaussian Markov process. The paper derives asymptotic simultaneous confidence regions for the parameters of the Ornstein Uhlenbeck process as an application.

Reviews

Required fields are marked *. Your email address will not be published.