An introduction to the analysis and design of maximum likelihood and robust estimators and filters. Maximum likelihood estimation theory: consistency, asymptotic efficiency, sufficiency. Robust estimation theory: qualitative robustness, breakdown point, influence function, change-of-variance function. Robust estimators: M-estimators, generalized M-estimators, high-breaddown estimators.
Robust estimation theories have undergone important developments that need to be introduced in various engineering fields such as signal processing, communications, radar systems and electric power systems, to cite a few. The cornerstones of these developments are the robustness concepts of breakdown point and influence function that enable the student to perform the analysis and design of estimators with desired requirements in view of their applications to engineering problems.
Prerequisites: ECE 5605
This course requires a working knowledge of probability and stochastic processes as taught in 5605.
Percentage of Course
|Probability distribution theory||10%|
|Robust estimators of location and scale||10%|
|Maximum Likelihood estimation theory and methods||15%|
|Robust estimation theories and methods||20%|
|Robust estimators in regression and applications||15%|
|Robust estimation of ARMA models and applications||10%|
|Robust Kalman filter and applications||10%|
|Estimation of Fractional ARIMA models and applications||10%|