Bootstrap Techniques for Signal Processing

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9780521831277: Bootstrap Techniques for Signal Processing
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The statistical bootstrap is one of the methods that can be used to calculate estimates of a certain number of unknown parameters of a random process or a signal observed in noise, based on a random sample. Such situations are common in signal processing and the bootstrap is especially useful when only a small sample is available or an analytical analysis is too cumbersome or even impossible. This book covers the foundations of the bootstrap, its properties, its strengths and its limitations. The authors focus on bootstrap signal detection in Gaussian and non-Gaussian interference as well as bootstrap model selection. The theory developed in the book is supported by a number of useful practical examples written in MATLAB. The book is aimed at graduate students and engineers, and includes applications to real-world problems in areas such as radar and sonar, biomedical engineering and automotive engineering.

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'... an excellent text for a graduate level engineering course or a useful reference for those who wish to apply bootstrap techniques in their work.' Annals of Biomedical Engineering

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The statistical bootstrap is a powerful tool in signal processing. This book covers the foundations of the bootstrap, its properties, its strengths, its limitations and model selection. The theory developed in the book is supported by a number of useful practical examples written in MATLAB.

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2010. Hardback. Condition: New. Language: English . Brand New Book ***** Print on Demand *****.The statistical bootstrap is one of the methods that can be used to calculate estimates of a certain number of unknown parameters of a random process or a signal observed in noise, based on a random sample. Such situations are common in signal processing and the bootstrap is especially useful when only a small sample is available or an analytical analysis is too cumbersome or even impossible. This book covers the foundations of the bootstrap, its properties, its strengths and its limitations. The authors focus on bootstrap signal detection in Gaussian and non-Gaussian interference as well as bootstrap model selection. The theory developed in the book is supported by a number of useful practical examples written in MATLAB. The book is aimed at graduate students and engineers, and includes applications to real-world problems in areas such as radar and sonar, biomedical engineering and automotive engineering. Seller Inventory # APC9780521831277

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2010. Hardback. Condition: New. Language: English . Brand New Book ***** Print on Demand *****. The statistical bootstrap is one of the methods that can be used to calculate estimates of a certain number of unknown parameters of a random process or a signal observed in noise, based on a random sample. Such situations are common in signal processing and the bootstrap is especially useful when only a small sample is available or an analytical analysis is too cumbersome or even impossible. This book covers the foundations of the bootstrap, its properties, its strengths and its limitations. The authors focus on bootstrap signal detection in Gaussian and non-Gaussian interference as well as bootstrap model selection. The theory developed in the book is supported by a number of useful practical examples written in MATLAB. The book is aimed at graduate students and engineers, and includes applications to real-world problems in areas such as radar and sonar, biomedical engineering and automotive engineering. Seller Inventory # APC9780521831277

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