第九版，Robert V. Hogg. Elliot A. Tanis. Dale L. Zimmerman

信号处理估计理论经典书籍,fundamental of statistical signal processing estimation theory

Fundamentals of Statistical Signal Processing: Practical Algorithm Development is the third volume in a series of textbooks by the same name. Previous volumes described the underlying theory of estimation and detection algorithms. In contrast, the current volume addresses the practice of converting this theory into software algorithms that may be implemented on a digital computer. In describing the methodology and techniques, it will not be assumed that the reader has studied the first two volumes, but of course, he/she is certainly encouraged to do so. Instead, the descriptions will focus on the general concepts using a minimum of mathematics but will be amply illustrated using MATLAB implementations. It is envisioned that the current book will appeal to engineers and scientists in industry and academia who would like to solve statistical signal processing problems through design of well-performing and implementable algorithms for real systems.

Statistical Modeling by Wavelets ISBN: 0471293652 Title: Statistical Modeling by Wavelets Author: Brani Vidakovic Publisher: Wiley-Interscience 3.2 Mb Format: pdf A comprehensive, step-by-step introduction to wavelets in statistics. What are wavelets? What makes them increasingly indispensable in statistical nonparametrics? Why are they suitable for "time-scale" applications? How are they used to solve such problems as denoising, regression, or density estimation? Where can one find up-to-date information on these newly "discovered" mathematical objects? These are some of the questions Brani Vidakovic answers in Statistical Modeling by Wavelets. Providing a much-needed introduction to the latest tools afforded statisticians by wavelet theory, Vidakovic compiles, organizes, and explains in depth research data previously available only in disparate journal articles. He carefully balances both statistical and mathematical techniques, supplementing the material with a wealth of examples, more than 100 illustrations, and extensive references-with data sets and S-Plus wavelet overviews made available for downloading over the Internet. Both introductory and data-oriented modeling topics are featured, including: * Continuous and discrete wavelet transformations. * Statistical optimality properties of wavelet shrinkage. * Theoretical aspects of wavelet density estimation. * Bayesian modeling in the wavelet domain. * Properties of wavelet-based random functions and densities. * Several novel and important wavelet applications in statistics. * Wavelet methods in time series. Accessible to anyone with a background in advanced calculus and algebra, Statistical Modeling by Wavelets promises to become the standard reference for statisticians and engineers seeking a comprehensive introduction to an emerging field. 在下面网址能下到本书相关的代码和数据集： http://www2.isye.gatech.edu/~brani/.public_html/wiley.html