Fisher information and maximum-likelihood estimation of covariance parameters in Gaussian stochastic processes

1998 ◽  
Vol 26 (1) ◽  
pp. 127-137 ◽  
Author(s):  
Markus Abt ◽  
William J. Welch
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Stochastic Processes ◽  
Fisher Information ◽  
Likelihood Estimation ◽  
Gaussian Stochastic Processes

Maximum likelihood estimation for continuous-time stochastic processes

1976 ◽  
Vol 8 (4) ◽  
pp. 712-736 ◽  
Author(s):  
Paul David Feigin
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Stochastic Processes ◽  
Continuous Time ◽  
Asymptotic Theory ◽  
Likelihood Estimation ◽  
Diffusion Approximations ◽  
Limit Theory

This paper is mainly concerned with the asymptotic theory of maximum likelihood estimation for continuous-time stochastic processes. The role of martingale limit theory in this theory is developed. Some analogues of classical statistical concepts and quantities are also suggested. Various examples that illustrate parts of the theory are worked through, producing new results in some cases. The role of diffusion approximations in estimation is also explored.

Maximum likelihood estimation for continuous-time stochastic processes

1976 ◽  
Vol 8 (04) ◽  
pp. 712-736 ◽  
Author(s):  
Paul David Feigin
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Stochastic Processes ◽  
Continuous Time ◽  
Asymptotic Theory ◽  
Likelihood Estimation ◽  
Diffusion Approximations ◽  
Limit Theory

This paper is mainly concerned with the asymptotic theory of maximum likelihood estimation for continuous-time stochastic processes. The role of martingale limit theory in this theory is developed. Some analogues of classical statistical concepts and quantities are also suggested. Various examples that illustrate parts of the theory are worked through, producing new results in some cases. The role of diffusion approximations in estimation is also explored.

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