Table of Contents

I. Modern Spectral Estimation, Theory and Application (with disk)
Prentice Hall Englewood Cliffs, 1988, 543 pages, ISBN 0-13- 598582-X


1.) Introduction

2.) Review of Linear and Matrix Algebra

3.) Review of Probability, Statistics, and Random Processes

4.) Classical Spectral Estimation

5.) Parametric Modeling

6.) Autoregressive Spectral Estimation: General

7.) Autoregressive Spectral Estimation: Methods

8.) Moving Average Spectral Estimation

9.) Autoregressive Moving Average Spectral Estimation: General

10.) Autoregressive Moving Average Spectral Estimation: Methods

11.) Minimum Variance Spectral Estimation

12.) Summary of Spectral Estimators

13.) Sinusoidal Parameter Estimation

14.) Multichannel Spectral Estimation

15.) Two-Dimensional Spectral Estimation

16.) Other Applications of Spectral Estimation Methods

Note about purchase:
Modern Spectral Estimation: Theory and Application (1988) by S. Kay is now available in paperback. It must be ordered through a bookstore using the ISBN# 0-13-015159-9.
If you have further difficulty obtaining a copy, please let me know.
Best regards,
Steve Kay


II. Fundamentals of Statistical Signal Processing, Estimation Theory, Prentice Hall Englewood Cliffs, 1993, 595 pages, ISBN 0-13- 345711-7


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

15.) Extension for Complex Data and Parameters


III. Fundamentals of Statistical Signal Processing, Vol. II - Detection Theory, Prentice Hall, Upper Saddle River, 1998, 560 pages, ISBN 0-13- 504135-X


1.) Introduction

2.) Summary of Important PDFs

3.) Statistical Decision Theory I

4.) Deterministic Signals

5.) Random Signals

6.) Statistical Decision Theory II

7.) Deterministic Signals with Unknown Parameters

8.) Random Signals with Unknown Parameters

9.) Unknown Noise Parameters

10.) NonGaussian Noise

11.) Summary of Detectors

12.) Model Change Detection

13.) Complex/Vector Extensions, and Array Processing


IV. Intuitive Probability and Random Processes using MATLAB, Springer, 2006, 833 pages, ISBN 0-387-24157-4


1.) Introduction

2.) Computer Simulation

3.) Basic Probability

4.) Conditional Probability

5.) Discrete Random Variables

6.) Expected Values for Discrete Random Variables

7.) Multiple Discrete Random Variables

8.) Conditional Probability Mass Function

9.) Discrete N-Dimensional Random Variables

10.) Continuous Random Variables

11.) Expected Values for Continuous Random Variables

12.) Multiple Continuous Random Variables

13.) Conditional Probability Density Functions

14.) Continuous N-Dimensional Random Variables

15.) Probability and Moment Approximations Using Limit Theorems

16.) Basic Random Processes

17.) Wide Sense Stationary Random Processes

18.) Linear Systems and Wide Sense Stationary Random Processes

19.) Multiple Wide Sense Stationary Random Processes

20.) Gaussian Random Processes

21.) Poisson Random Processes

22.) Markov Chains