Table of Content

1. Introduction.
2. Minimum Variance Unbiased Estimation.
3. Cramer-Rao Lower Bound.
4. Linear Models.
5. General Minimum Variance Unbiased Estimation.
6. Best Linear Unbiased Estimators.
7. Maximum Likelihood Estimation.
8. Least Squares.
9. Method of Moments.
10. The Bayesian Philosophy.
11. General Bayesian Estimators.
12. Linear Bayesian Estimators.
13. Kalman Filters.
14. Summary of Estimators.

Salient Features

• Describes the field of parameter estimation based on time series data.
• Provides a summary of principal approaches as well as a “roadmap” to use in the selection of an estimator.
• Extends many of the results for real data/real parameters to complex data/complex parameters.
• Summarizes as examples many of the important estimators used in practice.