Essential Wavelets for Statistical Applications and Data Analysis

Selection by Hypothesis Testing.- Recursive Testing.- Minimizing False Discovery.- 8.3 Cross-Validation Methods.- 8.4 Bayesian Methods.- 9 Generalizations and Extensions.- 9.1 Two-Dimensional Wavelets.- 9.2 Wavelet Packets.- Wavelet Packet Functions.- The Best Basis Algorithm.- 9.3 Translation Invariant Wavelet Smoothing.- References.- Glossary of Notation.- Glossary of Terms.

its easy style, many included examples, and elucidating discussion of problems presented." -Applications of Mathematics"The present book can be certainly recommended to those interested in statistical applications.... [The] approach needed for statistical applications is provided here quite well requiring only basics of statistical theory and familiarity with calculus and linear algebra.... This reader finds Chapters 7 and 8 especially useful for statisticians. What are statisticians doing with wavelets currently is discussed here." -The Journal of the Indian Institute of Science"The aim of the book is to present an accessible introductory survey of new tools of wavelet analysis and how they can be applied to basic data analysis problems such as signal processing, image analysis, data compression, etc. The author shows how these problems can be solved by fast algorithms which are of a simple form. Solutions of many practical examples are presented.... The book is ideal for a braod audience including advanced undergraduate students and graduates.... Also, professionals in statistics, researchers, and engineers who use methods of data analysis and their applications in statistics will learn about new wavelet methods." -Mathematica Bohemica "Accessible, clearly presented background material. Examples presented throughout the book. Variety of statistical applications. Intuitive style of presentation. Website for the book with additional resources includes S-plus software code functions for graphics used in the book." -L'enseignement Mathématique"As an accessible work that explains the decomposition and reconstruction of wavelet algorithms as applied to statistical data, Ogden's book is one of the growing number on wavelets. But this book is in a class of its own: it concentrates solely on the application of wavelets to statistics and data analysis. Ogden gives a brief introduction to basic theory, and then provides generous examples of wavelets and how they are constructed. He briefly compares Fourier methods, then discusses statistical applications, such as density estimation, estimation of regression functions through the application of the convolution-type kernel function, statistical testing, and Bayesian methods.... The book could conveniently be used by scientists who wish to apply wavelet methods to data analysis and by graduate students as a supplementary text. Recommended. Graduates through faculty and professionals." -Choice

"The book is...an introduction to [wavelets'] successful applications in statistics and data analysis. Only a limited knowledge of calculus, linear algebra, and elementary statistics is assumed. The book is thus accessible to advanced undergraduate students and graduate students as well as to applied statisticians and engineers concerned with data treatment and analysis. An appendix on vector spaces, a glossary of terms and notation, an index, and a long list ofreferences close the book, which will certainly be appreciated by a large circle of readers for its easy style, many included examples, and elucidating discussion of problems presented." -Applications of Mathematics

"The present book can be certainly recommended to those interested in statistical applications.... [The] approach needed for statistical applications is provided here quite well requiring only basics of statistical theory and familiarity with calculus and linear algebra.... This reader finds Chapters 7 and 8 especially useful for statisticians. What are statisticians doing with wavelets currently is discussed here." -The Journal of the Indian Institute of Science

"The aim of the book is to present an accessible introductory survey of new tools of wavelet analysis and how they can be applied to basic data analysis problems such as signal processing, image analysis, data compression, etc. The author shows how these problems can be solved by fast algorithms which are of a simple form. Solutions of many practical examples are presented.... The book is ideal for a braod audience including advanced undergraduate students and graduates.... Also, professionals in statistics, researchers, and engineers who use methods of data analysis and their applications in statistics will learn about new wavelet methods." -Mathematica Bohemica 

"Accessible, clearly presented background material. Examples presented throughout the book. Variety of statistical applications. Intuitive style of presentation. Website for the book with additional resources includes S-plus software code functions for graphics used in the book." -L'enseignement Mathématique

"As an accessible work that explains the decomposition and reconstruction of wavelet algorithms as applied to statistical data, Ogden's book is one of the growing number on wavelets. But this book is in a class of itsown: it concentrates solely on the application of wavelets to statistics and data analysis. Ogden gives a brief introduction to basic theory, and then provides generous examples of wavelets and how they are constructed. He briefly compares Fourier methods, then discusses statistical applications, such as density estimation, estimation of regression functions through the application of the convolution-type kernel function, statistical testing, and Bayesian methods.... The book could conveniently be used by scientists who wish to apply wavelet methods to data analysis and by graduate students as a supplementary text. Recommended. Graduates through faculty and professionals."   -Choice