Authors:
- Provides a wide audience with the fundamentals of statistical signal analysis in an accessible yet rigorous manner
- Includes two new chapters that present wavelets and the uncertainty principle, and the forecasting problems for stationary time series
- Considerably expands on previous editions with new examples, illustrations, and commentary
- Contains a large appendix with solutions of selected problems
Part of the book series: Statistics for Industry, Technology, and Engineering (SITE)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
About this book
This essentially self-contained, deliberately compact, and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, explained in a concise, yet fairly rigorous presentation.
Topics and Features:
· Fourier series and transforms—fundamentally important in random signal analysis and processing—are developed from scratch, emphasizing the time-domain vs. frequency-domain duality;
· Basic concepts of probability theory, laws of large numbers, the central limit theorem, and statistical parametric inference procedures are presented so that no prior knowledge of probability and statistics is required; the only prerequisite is a basic two–three semester calculus sequence;
· Computer simulation algorithms of stationary random signals with a given power spectrum density;
· Complementary bibliography for readers who wish to pursue the study of random signals in greater depth;
· Many diverse examples and end-of-chapter problems and exercises.
Developed by the author over the course of many years of classroom use, A First Course in Statistics for Signal Analysis, Second Edition may be used by junior/senior undergraduates or graduate students in electrical, systems, computer, and biomedical engineering, as well as the physical sciences. The work is also an excellent resource of educational and training material for scientists and engineers working in research laboratories. This third edition contains two additional chapters that present wavelets and the uncertainty principle, and the forecasting problems for stationary time series. These two topics are essential for students to attain a deeper understanding of statistical analysis of random signals.Reviews from previous editions:
A First Course in Statistics for Signal Analysis is a small, dense, and inexpensive book that covers exactly what the title says: statistics for signal analysis. The book has much to recommend it. The author clearly understands the topics presented. The topics are covered in a rigorous manner, but not so rigorous as to be ostentatious. JASA (Review of the First Edition)
This is a nicely written self-contained book and it is a good candidate for adoption as a textbook for upper-level undergraduate and even for a graduate course for engineering and physical sciences students. … I have no hesitation in recommending it as a textbook for the targeted course and audience. Technometrics, Vol. 53 (4), November, 2011 (Review of the Second Edition)
Authors and Affiliations
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Cleveland, USA
Wojbor A. Woyczyński
About the author
Bibliographic Information
Book Title: A First Course in Statistics for Signal Analysis
Authors: Wojbor A. Woyczyński
Series Title: Statistics for Industry, Technology, and Engineering
DOI: https://doi.org/10.1007/978-3-030-20908-7
Publisher: Birkhäuser Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-20907-0Published: 17 October 2019
Softcover ISBN: 978-3-030-20910-0Published: 17 October 2020
eBook ISBN: 978-3-030-20908-7Published: 04 October 2019
Series ISSN: 2662-5555
Series E-ISSN: 2662-5563
Edition Number: 3
Number of Pages: XVIII, 332
Number of Illustrations: 26 b/w illustrations, 69 illustrations in colour
Topics: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Fourier Analysis, Signal, Image and Speech Processing