FUNDAMENTALS OF STATISTICAL SIGNAL PROCESSING VOLUME 1

STEVEN M KAY

ISBN 10: 8131728994 / ISBN 13: 9788131728994

Published by Pearson, 2009

About this Item

International Edition, Brand New, ISBN and Cover same but contents similar to U.S. Edition, Printed in Black & White. End Chapter Exercises may differ. We Ship From Multiple Locations Including India. No CD/Access code. Legal to use despite any disclaimer, We ship to PO , APO and FPO adresses in U.S.A .Choose Expedited Shipping for FASTER DELIVERY.Customer Satisfaction Guaranteed 0.88. Seller Inventory # INISB1#9788131728994

Bibliographic Details

Title: FUNDAMENTALS OF STATISTICAL SIGNAL ...

Publisher: Pearson

Publication Date: 2009

Binding: Paperback

Condition: NEW

Edition: International Edition

About this title

Synopsis:

Description This book is written onparameter estimation range from highly theoretical to morepractical applied statistics. This book strikes a balance betweenthese 2 extremes. Audience is community involved in design andimplementation of signal processing algorithms. The primary focusis on obtaining optimal estimation algorithms that may beimplemented in a computer. Numerous, worked-out examples. The bookis rich in examples that illustrate the theory (of statisticalsignal processing) and examples that apply the theory to actualsignal processing problems of current interest. For Sale in Indiansubcontinent only Describes the field of parameter estimation based on time series data. Provides a summary of principal approaches as well as a "roadmap" to use in the selection of an estimator. Extends many of the results for real data/real parameters to complex data/complex parameters. Summarizes as examples many of the important estimators used in practice. Introduction. Minimum Variance Unbiased Estimation. Cramer-Rao Lower Bound. Linear Models. General Minimum Variance Unbiased Estimation. Best Linear Unbiased Estimators. Maximum Likelihood Estimation. Least Squares. Method of Moments. The Bayesian Philosophy. General Bayesian Estimators. Linear Bayesian Estimators. Kalman Filters. Summary of Estimators.