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Fundamentals of Statistical Signal Processing Vol 1: Estimation Theory
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by
Steven M Kay
For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals - radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc. A unified presentation of parameter estimation for those ...
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Great Estimations
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by
Bruce Goldstone
How many jelly beans are on this book's cover? Don't count--estimate! If someone handed you a big bowl of jelly beans, how would you figure out how many there are? You could count them, one by one--or you could estimate. Do you see more than five jelly beans? Less than a million? This unique book will show you how to train your eyes and your mind ...
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Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin
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by
Lawrence Weinstein, John A Adam
"Guesstimation" is a book that unlocks the power of approximation - it's popular mathematics rounded to the nearest power of ten! The ability to estimate is an important skill in daily life. More and more leading businesses today use estimation questions in interviews to test applicants' abilities to think on their feet. "Guesstimation" enables ...
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Betcha!
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by
Stuart J Murphy
What do cars, toys, people, and jelly beans have in common? They can all be estimated. Two friends try out their skills and find out that estimating can have real rewards--especially when there's a contest to enter! Full color.
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Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory
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by
Steven M Kay
The most comprehensive overview of signal detection available. This is a thorough, up-to-date introduction to optimizing detection algorithms for implementation on digital computers. It focuses extensively on real-world signal processing applications, including state-of-the-art speech and communications technology as well as traditional sonar ...
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Linear Models
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by
Shayle Robert Searle
This 1971 classic on linear models is once again available--as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.
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Density Estimation for Statistics and Data Analysis
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by
Bernard W Silverman, Silverman Silverman
An exposition of density estimation for statistics and data analysis. A volume in the "Monographs on Statistics and Applied Probability" series, it is designed for applied statisticians
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Random Signals: Detection, Estimation and Data Analysis
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by
Sam Shanmugan, Arthur M Breipohl, K Sam Shanmugan
This treatise develops the theory of random processes and its application to the study of systems and the analysis of random data. The text covers three important areas: fundamentals and examples of random process models; application of probabilistic models - signal detection and filtering; and statistical estimation - the measurement and analysis ...
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State Estimation in Electric Power Systems: A Generalized Approach
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by
A Monticelli
"State Estimation in Electric Power Systems: A Generalized Approach" provides for the first time a comprehensive introduction to the topic of state estimation at an advanced textbook level. The theory as well as practice of weighted least squares (WLS) is covered with significant rigor. Included is an in-depth analysis of power flow basics, proper ...
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Probability, Random Processes, and Estimation Theory for Engineers
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by
Henry Stark
Revision of our best-selling probability and random processes book (vs. Papoulis).
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Nonlinear filtering and smoothing an introduction to martingales, stochastic integrals and estimation
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by
Krishnan
Appropriate for upper-level undergraduates and graduate students, this volume addresses the fundamental concepts of martingales, stochastic integrals, and estimation. Written by an engineer for engineers, it emphasizes applications.
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Factorization Methods for Discrete Sequential Estimation
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by
Gerald J Bierman
This estimation reference text thoroughly describes matrix factorization methods successfully employed by numerical analysts, familiarizing readers with the techniques that lead to efficient, economical, reliable, and flexible estimation algorithms. Geared toward advanced undergraduates and graduate students, this pragmatically oriented ...
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Stochastic processes and filtering theory
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by
Andrew H. Jazwinski
This book presents a unified treatment of linear and nonlinear filtering theory for engineers, with sufficient emphasis on applications to enable the reader to use the theory. The need for this book is twofold. First, although linear estimation theory is relatively well known, it is largely scattered in the journal literature and has not been ...
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Estimation Problems in Hybrid Systems
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by
David D Sworder, John E Boyd
Recent developments in sensor and processor sophistication have created a need for effective estimation and control algorithms for hybrid, nonlinear systems. This book presents an effective, flexible family of estimation algorithms that can be used in estimating or controlling a variety of nonlinear plants. Several applications are studied, ...
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Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software
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by
Yaakov Bar-Shalom, Xiao-Rong Li, Thiagalingam Kirubarajan
Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, ...
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Lessons in Estimation Theory for Signal Processing, Communications, and Control
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by
Dr. Jerry M Mendel
This book covers key topics in parameter estimation and state estimation, with supplemental lessons on sufficient statistics and statistical estimation of parameters, higher-order statistics and a review of state variable models. It also links computations into MATLAB and its associated toolboxes. A small number of important estimation M-files, ...
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Stochastic Models, Estimation & Control
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by
Peter S Maybeck
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Image Models (and Their Speech Model Cousins)
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by
Stephen E Levinson, Larry Shepp (Editor)
This volume explores the interface between two diverse areas of applied mathematics which are both 'customers' of the maximum likelihood methodology; emission tomography and hidden Markov models as an approach to speech understanding. Other areas where maximum likelihood is used in this volume include parsing of text (Jelinek), microstructure of ...
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Parallel Scientific Computing and Optimization: Advances and Applications
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by
Raimondas Ciegis (Editor), David Henty (Editor), Bo Ka Gstrom (Editor)
This work introduces new developments in the construction, analysis, and implementation of parallel computing algorithms. This book presents 23 self-contained chapters, including surveys, written by distinguished researchers in the field of parallel computing. Each chapter is devoted to some aspects of the subject such as parallel algorithms for ...
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The Jacknife, the Bootstrap, and Other Resampling Plans
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by
Bradley Efron
The jackknife and the bootstrap are nonparametric methods for assessing the errors in a statistical estimation problem. They provide several advantages over the traditional parametric approach: the methods are easy to describe and they apply to arbitrarily complicated situations; distribution assumptions, such as normality, are never made. This ...
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Generalized Method of Moments Estimation
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by
Laszlo Matyas (Editor), Peter C B Phillips (Editor), Christian Gourieroux (Editor)
The generalized method of moments (GMM) estimation has emerged as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. The principal objective of this volume is to offer a complete presentation of the theory of GMM estimation as well as insights into ...
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Statistical Methods for Social Scientists
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by
Eric A Hanushek, John E Jackson, Erik A Hanushek
The aspects of this text which we believe are novel, at least in degree, include: an effort to motivate different sections with practical examples and an empirical orientation; an effort to intersperse several easily motivated examples throughout the book and to maintain some continuity in these examples; and the extensive use of Monte Carlo ...
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Introduction to robust estimation and hypothesis testing
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by
Rand R Wilcox
This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief ...
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Estimation of Dependences Based on Empirical Data: Empirical Inference Science
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by
Vladimir Vapnik, S Kotz (Translator)
In 1982, Springer published the English translation of the Russian book, "Estimation of Dependencies Based on Empirical Data" which became the foundation of the statistical theory of learning and generalization (the VC theory). A number of new principles and new technologies of learning, including SVM technology, have been developed based on this ...
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The Em Algorithm and Extensions
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by
Geoffrey J McLachan, Thriyambakam Krishnan
This book presents an organized and well-knit account of the theory, methodology, extensions, and major applications of the Expectation-Maximization (EM) algorithm. It includes applications in the standard statistical contexts such as regression, factor analysis, variance components estimation, repeated-measures designs, categorical data analysis, ...
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