Book Description
Intuitive Probability and Random Processes using MATLAB® is an introduction to probability and random processes that merges theory with practice. Based on the author’s belief that only "hands-on" experience with the material can promote intuitive understanding, the approach is to motivate the need for theory using MATLAB examples, followed by theory and analysis, and finally descriptions of "real-world" examples to acquaint the reader with a wide variety of applications. The latter is intended to answer the usual question "Why do we have to study this?" Other salient features are:
*heavy reliance on computer simulation for illustration and student exercises
*the incorporation of MATLAB programs and code segments
*discussion of discrete random variables followed by continuous random variables to minimize confusion
*summary sections at the beginning of each chapter
*in-line equation explanations
*warnings on common errors and pitfalls
*over 750 problems designed to help the reader assimilate and extend the concepts
Intuitive Probability and Random Processes using MATLAB® is intended for undergraduate and first-year graduate students in engineering. The practicing engineer as well as others having the appropriate mathematical background will also benefit from this book.
About the Author
Steven M. Kay is a Professor of Electrical Engineering at the University of Rhode Island and a leading expert in signal processing. He has received the Education Award "for outstanding contributions in education and in writing scholarly books and texts..." from the IEEE Signal Processing society and has been listed as among the 250 most cited researchers in the world in engineering.
Customer Reviews:
Pretty good book, worth buying.......2007-09-12
This is a good book, I have been reading it, so far I enjoy it. compared with other book on the same topic, this book is worth buying.
Very pedagogical exposition.......2007-09-03
you will get a solid understanding of each topic-chapter title. Not only engineering but also math students should be trained in this spirit. Each chapter culminates in a real world application which is indeed interesting, and not completely trivial. Few books teach the theory but also bring you to the level where you can apply this theory to problems. However, there is a drawback in my opinion, since the book is supposed to address grad students some mathematical sophistication could be used so as to achieve a more concise presentation. I don't blame the author for extreme clarity of course but usually you want to get to the interesting stuff at a much higher pace. At some points one could say the book is a bit verbose. Overall, it is almost excellent taking into account its target-group and the material it deals with.
Excellent Book.......2007-03-09
This is a very good book for classes and suitable for textbook. I recomend this book for who is the new in the probability. The book is readable, clear explaination and MATLAB examples are understandable.
N.A.S.
Intuitive as it says.......2006-11-10
It's a great book for probability. Explain the abstract concept in an easy way. You will find out that you go through all those difficulties just by following the author. I got full point in my first quiz which was never the case for my math courses.
An Excellent Book!.......2006-02-21
This is a much needed book that bridges theory and numerical computations. Theory needs to be supplemented with numerical applications to get a well-grounded understanding, especially in Probability Theory and Stochastic Processes.
Average customer rating:
- informal treatise for an undergrad
- Useful (although expensive) introduction to SSA
- Brilliant exposition and crystal clarity
|
Singular Spectrum Analysis: A New Tool in Time Series Analysis (Language of Science)
J.B. Elsner , and
A.A. Tsonis
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover
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ASIN: 0306454726 |
Book Description
This original new text provides an easily accessible introduction to this important new topic in time series analysis. The authors emphasize examples over theoretical explanations and the need for proper and careful statistical tests in the context of data exploration. The book's focus is on the application of the method in signal detection, filtering, and prediction. Instructors and students will appreciate the step-by-step presentation of underlying ideas.
Customer Reviews:
informal treatise for an undergrad.......2006-05-29
Elsner and Tsonis give a foray into an application of linear algebra. You need to know how to diagonalise a square matrix, and that this process amounts to finding the eigenvalues of that matrix. The book is brief, and is written at a pace that should be accessible to a maths or physics undergrad. The explanations are not as terse as those typically found in journal papers. This is meant to be a textbook, after all.
However, for a maths text, there is no attempt at a formal theorem-based exposition. The book reads more like an informal treatise whose purpose is to get you to grasp and use the basic ideas, without worrying too much about the rigour.
It might also help to have a maths package available whilst going through the book. It will let you run the book's methods on your data in a quick manner. Instead of having to code from scratch.
Useful (although expensive) introduction to SSA.......2001-05-26
This text is divided into three main parts: 1) Mathematical notes, 2) Theory and Methods, 3) Applications. As a reader interested in time series analysis but unfamiliar with singular spectrum analysis (SSA) per se, I was not really sure what SSA was exactly, or what it is suppose to accomplish, until nearly halfway into the book. Very early on, I found no one single sentence, paragraph, definition, or description that clearly defined SSA beyond a "technique based on spectral decomposition."
Although the text is clearly written and well organized, the authors' target audience is not well established. For example, Section I starts with an rather remedial primer on linear matrix algebra, yet Section II already seems to assume the reader has a fairly mature knowledge of statistical inference, hypothesis testing, and principal component analysis (which might be gained from courses on statistical analysis of variance, or ANOVA). The authors also make frequent references to other time series analysis techniques, such as ARMA modeling and Fourier analysis in Section III. Familiarity with ANOVA and other kinds of time series analyses will certainly benefit the reader.
One gets the impression that the manuscript has been enhanced to fill a textbook-sized volume (and would explain why the book dedicates an entire chapter to numerical examples illustrating such things as how to multiply two matrices). The font size is strikingly large for a 6" x 9" textbook, and the layout is noticeably fragmented with lots of section subtitles and large amounts of white space around these section titles. Specifically, each page accommodates no more than 34 lines of 4 1/4" wide text, which I compared with several textbooks this size which usually accommodated 40-45 lines of 4 1/2" wide text (I suspect the 153 page content might have been fitted into about 100 pages just by changing the book layout). Given the quite basic subject matter of the first few chapters, the remainder of the book is but a fairly short survey on the main subject of SSA. The advantage is that its contents can be covered relatively quickly.
The rarity of introductory texts specifically dedicated to this niche subject matter, and the uncomplicated presentation the authors have chosen, make this book a worthwhile, albeit expensive, introduction to the topic. This book has nice presentation qualities, and had it been issued as a thin, inexpensive paperback, this would more easily be a 4 or 5 star book. Similarly, this is a 4 star book for the prospective reader analyzing data related to the atmospheric sciences or climatology (as much of the authors' background materials emphasize these areas). An expanded second edition would be welcomed.
Brilliant exposition and crystal clarity.......1997-05-16
This book is a breath of fresh air for anyone who is attempting teach time series analysis to themselves; especially the Spectral Analysis techniques. The book has three primary strengths. First, it assembles between two covers EVERYTHING that one needs to understand the developement of Spectral Methods. Secondly, the authors write in a very clear manner with no attempt to obfuscate the material. The authors clearly want to teach these techniques rather than satisfy some publish or perish requirements. Thirdly, and perhaps most importantly, the book has detailed and completely worked out examples using actual numbers in the matrices. One may think symbology should be enough but actual numbers make it very easy to trace the logic . This is a terrific tome and destined to becoming a teaching classic. It has generrated the longest series (no pun intended) of
"Ah Ha!" experiences for me that I have had the pleasure of in a very long time. Why can not every text explain singular value decomposition the way these authors have? Showing the link between difference equations and differential equations and the segue into them is a work of art. I am very pleased with my purchase
Average customer rating:
- This book rocks the statistics world!
|
The Spectral Analysis of Time Series (Probability and Mathematical Statistics)
Lambert Herman Koopmans
Manufacturer: Academic Press
ProductGroup: Book
Binding: Paperback
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ASIN: 0124192513 |
Book Description
To tailor time series models to a particular physical problem and to follow the working of various techniques for processing and analyzing data, one must understand the basic theory of spectral (frequency domain) analysis of time series. This classic book provides an introduction to the techniques and theories of spectral analysis of time series. In a discursive style, and with minimal dependence on mathematics, the book presents the geometric structure of spectral analysis. This approach makes possible useful, intuitive interpretations of important time series parameters and provides a unified framework for an otherwise scattered collection of seemingly isolated results.
The books strength lies in its applicability to the needs of readers from many disciplines with varying backgrounds in mathematics. It provides a solid foundation in spectral analysis for fields that include statistics, signal process engineering, economics, geophysics, physics, and geology. Appendices provide details and proofs for those who are advanced in math. Theories are followed by examples and applications over a wide range of topics such as meteorology, seismology, and telecommunications.
Topics covered include Hilbert spaces; univariate models for spectral analysis; multivariate spectral models; sampling, aliasing, and discrete-time models; real-time filtering; digital filters; linear filters; distribution theory; sampling properties ofspectral estimates; and linear prediction.
Key Features
* Hilbert spaces
* univariate models for spectral analysis
* multivariate spectral models
* sampling, aliasing, and discrete-time models
* real-time filtering
* digital filters
* linear filters
* distribution theory
* sampling properties of spectral estimates
* linear prediction
Customer Reviews:
This book rocks the statistics world!.......1998-10-09
Koopmans is king when it comes to statistical analysis. Read this book.
Average customer rating:
- Novices Should Start Here
- An excellent manual for those ignoring everything about spec
- exceptionally clear
|
Spectral Analysis of Time-Series Data
Rebecca M. Warner
Manufacturer: The Guilford Press
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Similar Items:
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Fourier Analysis of Time Series: An Introduction (Wiley Series in Probability and Statistics)
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The Analysis of Time Series: An Introduction, Sixth Edition (Texts in Statistical Science)
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Spectral Analysis for Physical Applications
-
Time Series Analysis by State Space Methods (Oxford Statistical Science Series)
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Fourier Series and Orthogonal Functions
ASIN: 1572303387 |
Book Description
This book provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. It is written both for researchers and students new to the area and for those who have already collected time-series data but wish to learn new ways of understanding and presenting them. Facilitating the interpretation of observations of behavior, physiology, mood, perceptual threshold, social indicator variables, and other responses, the book focuses on practical applications and requires much less mathematical background than most comparable texts. Using real data sets and currently available software (SPSS for Windows), the author employs extensive examples to clarify key concepts. Topics covered include research design issues, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series. Also considered are theoretical questions, problems of interpretation, and potential sources of artifact.
Customer Reviews:
Novices Should Start Here.......2001-10-17
If you haven't got a clue about spectral analysis, this is the ideal place to start exploring the frequency domain. Although the examples are from psychological studies, this is not a reason why an economist shouldn't read it!!!
An excellent manual for those ignoring everything about spec.......2001-03-28
I think this book is extremely simple. The only knowledge required to understand it is perhaps Ordinary Least Squares. A theoretical explanation of the spectral analysis is not in the book. There is only a brief comment on De moivre's theorem (the one allowing and sustaining the whole spectral analysis) in a footnote. The main purpose of the author is to deliver an empirical methodology for empirical researchers not interested in the theories. The advantage is that, once you see such empirical applications, you understand the underlying idea of spectral Analysis. The elementary examples are very illuminating. The book is well organized and the review of "de-trending" (on this particular subject, I would like to say that the scientific discussion has evolved a lot in the last 20 years and the author's presentation is a little bit old), "harmonic analysis", "periodogrammes" seems pretty coherent. Perhaps the only drawback is that the text is a little repetitive and thus, slightly boring; but this is a minor problem, if you consider that this style will make more solids the understanding of the fundamental concepts. It's a great introduction to spectral analysis. Students having standard mathematical knowledge should begin here and then start reading more technical works, such as Bloomfied's "Fourier Analysis of Time Series" and the chapter of spectral analysis of Hamilton's "Analysis of Time Series".
exceptionally clear.......2000-04-06
No one writes more clearly than Rebecca Warner. If you want to understand spectral analysis, she will help you. If you want to *do* spectral analysis, read this book first. It might be the only one you need.
Average customer rating:
- More useful as a second book than as an introduction
|
Modern Spectrum Analysis of Time Series: Fast Algorithms and Error Control Techniques
Prabhakar S. Naidu
Manufacturer: CRC
ProductGroup: Book
Binding: Hardcover
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ASIN: 0849324645 |
Book Description
Spectrum analysis can be considered as a topic in statistics as well as a topic in digital signal processing (DSP). This book takes a middle course by emphasizing the time series models and their impact on spectrum analysis. The text begins with elements of probability theory and goes on to introduce the theory of stationary stochastic processes. The depth of coverage is extensive. Many topics of concern to spectral characterization of Gaussian and non-Gaussian time series, scalar and vector time series are covered. A section is devoted to the emerging areas of non-stationary and cyclostationary time series. The book is organized more as a textbook than a reference book. Each chapter includes many examples to illustrate the concepts described. Several exercises are included at the end of each chapter. The level is appropriate for graduate and research students.
Customer Reviews:
More useful as a second book than as an introduction.......2001-03-17
Primarily based on a good set of topics in the table of contents, I chose this book in an earlier semester as the text for a graduate level course in spectral analysis. The assumed background for our students is basic experience with Fourier transforms, general statistics, and Fortran programming. The students found this book almost useless, though, because it is at too high a level for a first course. I did not find it nearly as useful as the classic by Jenkins and Watts "Spectral Analysis and its Applications" or the first edition of the book by Peter Bloomfield, "Fourier Analysis of Time Series." (I haven't seen the second edition of Bloomfield yet.) In my opinion, Naidu's book is better for people who already have experience with time series analysis and who want to supplement their knowledge. There is a good list of references at the end of each chapter, and a number of topics go beyond "classical" techniques, as you would expect from the book's title.
Average customer rating:
|
Spectral Methods in Econometrics
George Samuel Fishman
Manufacturer: Harvard University Press
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ASIN: 0674831918 |
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Automatic Autocorrelation and Spectral Analysis
Piet M.T. Broersen
Manufacturer: Springer
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Binding: Hardcover
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ASIN: 1846283280 |
Book Description
Automatic Autocorrelation and Spectral Analysis gives random data a language to communicate the information they contain objectively. In the current practice of spectral analysis, subjective decisions have to be made all of which influence the final spectral estimate and mean that different analysts obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that solution is only acceptable if it is close to the best attainable accuracy for most types of stationary data. Automatic Autocorrelation and Spectral Analysis describes a method which fulfils the near-optimal-solution criterion. It takes advantage of greater computing power and robust algorithms to produce enough models to be sure of providing a suitable candidate for given data. Improved order selection quality guarantees that one of the best (and often the best) will be selected automatically. The data themselves suggest their best representation but should the analyst wish to intervene, alternatives can be provided. Written for graduate signal processing students and for researchers and engineers using time series analysis for practical applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis, this text offers: tuition in how power spectral density and the autocorrelation function of stochastic data can be estimated and interpreted in time series models; extensive support for the MATLAB® ARMAsel toolbox; applications showing the methods in action; appropriate mathematics for students to apply the methods with references for those who wish to develop them further.
Customer Reviews:
Wish I'd bought it sooner.......2000-05-26
Spectral Analysis and Its Applications is an excellent source for professionals with intermediate time-series statistics skills. It clearly outlines and demonstrates the methods necessary to carry out sound spectral analysis of time-series data and how to use this data for modelling purposes. It is also a great aid and reference for anyone who needs the means to triumph over inexperience and under-education in time-series debates. Four years out of graduate school, I need this book again; I wish I'd bought it sooner.
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- Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition)
- Mechanics of Composite Materials, Second Edition (Mechanical Engineering)
- Mirror Symmetry (Clay Mathematics Monographs, V. 1)
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- Modeling Structured Finance Cash Flows with Microsoft Excel: A Step-by-Step Guide.Book & CD-ROM
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