Customer Reviews

Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory (v. 1)

5 star:		(11)
4 star:		(1)
3 star:		(1)
2 star:		(0)
1 star:		(0)

 

 

In this book, Steven M. Kay has produced an excellent tutorial and research reference book on estimation theory. The book covers enough introductory material for someone with a reasonable undergraduate understanding of statistics to pick up the ideas quickly. The theory is illustrated with very concrete examples; the examples give an "under-the-hood" insight into the solution of some common estimation problems in signal processing. If you're a statistician, you might not like this book. If you're an engineer, you will like it.

Without any hesitation, I consider this book as a masterpiece in the area of statistical signal processing. Kay takes the reader to the journey of estimation theory as if a science teacher takes his students to a field trip. The one special feature of this book is the convergence of thought that reader obtains upon reading the book. Kay lays a fundamental bridge between various estimators using his succinct style for describing the subject.

Few special areas require more attention in this book. For example the coverage of EM methods is very condense and requires more elaboration. Also there is no discussion on the estimation methods using higher order statistics.

Overall I consider this book as the best book I have read ever and I highly recommend this book to those who want to obtain an ever-lasting view on statistical signal processing.

I've had tough courses on statistical signal processing as a post-grade student. I am often confused in front of a problem and turning back to the notes taken in class doesn't help much.
When you read this book all gets bright. I am still wondering how some teachers can be so confusing while such good books do exist...
However don't count on it for in depth mathematical demonstrations, it starts with a practical problem and explains how to model things. Thus it is a bit bottom-up but anyway starting from a good graduate level in signal and stats.
I got this one at the library but already ordered a copy for myself and am planning to get part2 on detection.

The theory is explaned well and motivated, but what makes the book great are the examples. There are many worked examples and they are chosen to make things very clear.

This text is very good for those who start doing research in statistical signal processing. A lot of explanations, technical terms are well presented and consistent, plus a number of examples that help you to learn about different statistical signal processing concepts and algorithms. Research students can be beneficial alot from this text.

This is one of the best references on statistical signal processing. The topic is not of simple matter, but the author presents the materials clearly together with great examples. The book is reader-friendly and is relatively error-free. I have bought several copies for my PhD students at Georgia Tech.

This book was asigned to me for a graduate course in Statistical Signal Estimation. The book was very useful and easy to read. It was well written and had helpful examples. I recommend this book for any one who wants to learn about Estimation Theory.

This is an excellent book for someone who needs to pick up the essentials of estimation theory in a hurry. I have never taken a course on statistical signal processing or information theory, and yet I was able to learn the subject just from reading this textbook to the point where I can do graduate level research in the area. Kay makes the text very readable so one can just follow along as if attending lectures, and he does a brilliant job of striking the right balance of theory and real-world examples so you can really understand the material. I did not have enough time to try many of the exercise problems, but the ones that I did try were excellent. I was however able to gain enough understanding through the text and examples to actually put it into practice in my own research, so that is definitely the sign of a great textbook.

Very well written and easy to follow. The systematic approach to the various forms of estimators clears up what was was originally a 'mess' of estimators and gives concise summaries on the interrelation between them.

My only complaint is the poor quality binding which is already starting to fall apart after less than a three months of normal usage. For this price, a proper binding would have been in order.

I bought this book because I wanted an practical adjunct to accompany my study of linear modeling theory using Stapleton's "Linear Statistical Models." Stapleton's text is theoretical where results are explained. I had hoped Kay's book would provide some practical examples from engineering and technology. The problem was that Kay's book just states results. There is no development of the results from beginning theory with examples. Why should one make the assumptions he makes?

But this is just my take. From perusing the other reviews of Kay's book, he produces what some other reader's want.