I. Modern Spectral Estimation, Theory and Application
(with disk)
Prentice Hall Englewood Cliffs, 1988, 543 pages, ISBN 0-13-
598582-X
Chapters
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
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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
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II. Fundamentals of Statistical Signal Processing, Estimation
Theory, Prentice Hall Englewood Cliffs, 1993, 595
pages, ISBN 0-13- 345711-7
Chapters
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
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III. Fundamentals of Statistical Signal Processing, Vol. II - Detection Theory, Prentice Hall, Upper Saddle River, 1998, 560 pages, ISBN 0-13- 504135-X
Chapters
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
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IV. Intuitive Probability and
Random Processes using MATLAB®,
Springer, 2006,
833 pages, ISBN 0-387-24157-4
Chapters
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
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