Title :
Large deviations of consistent parameter estimates in diffusions
Author_Institution :
Dept. of Electr. Eng., McGill Univ., Montreal, Que., Canada
Abstract :
Rates of convergence of strongly consistent parameter estimates in diffusion processes are studied via large deviations (LD) laws for the suprema of the estimation error´s fail processes. First, conditional LD limits are obtained by utilizing a general martingale law. Those are then applied to derive simple stopping rules. Finally, unconditional LD lower bounds are derived by an extension of a well known direct method
Keywords :
convergence of numerical methods; diffusion; parameter estimation; probability; convergence rate; diffusion processes; large deviation laws; lower bounds; parameter estimation; probability; stopping rules; Convergence; Diffusion processes; Estimation error; Infinite horizon; Maximum likelihood estimation; Parameter estimation; Recursive estimation; Stochastic processes; Tail; Upper bound;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.411403
