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Robust Statistics for Signal Processing [Kõva köide]

(Aalto University, Finland), (Aalto University, Finland), (Technische Universität, Darmstadt, Germany), (Technische Universität, Darmstadt, Germany)
  • Formaat: Hardback, 312 pages, kõrgus x laius x paksus: 253x178x18 mm, kaal: 770 g
  • Ilmumisaeg: 08-Nov-2018
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107017416
  • ISBN-13: 9781107017412
Teised raamatud teemal:
Robust Statistics for Signal ProcessingSuurem pilt
  • Formaat: Hardback, 312 pages, kõrgus x laius x paksus: 253x178x18 mm, kaal: 770 g
  • Ilmumisaeg: 08-Nov-2018
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107017416
  • ISBN-13: 9781107017412
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781107017412.html
Märksõnad:
Understand the benefits of robust statistics for signal processing with this authoritative yet accessible text. The first ever book on the subject, it provides a comprehensive overview of the field, moving from fundamental theory through to important new results and recent advances. Topics covered include advanced robust methods for complex-valued data, robust covariance estimation, penalized regression models, dependent data, robust bootstrap, and tensors. Robustness issues are illustrated throughout using real-world examples and key algorithms are included in a MATLAB Robust Signal Processing Toolbox accompanying the book online, allowing the methods discussed to be easily applied and adapted to multiple practical situations. This unique resource provides a powerful tool for researchers and practitioners working in the field of signal processing.

Moving from fundamental theory to cutting-edge advances in the field, gain a comprehensive understanding of the benefits that robust statistics bring to signal processing with this authoritative treatment of the subject. Real-world examples and a MATLAB Robust Signal Processing Toolbox allow for easy practical application of the methods described.

Muu info

Understand the benefits of robust statistics for signal processing using this unique and authoritative text.
Preface ix
Abbreviations xvi
List of Symbols
xx
1 Introduction and Foundations
1(27)
1.1 History of Robust Statistics
1(4)
1.2 Robust M-estimators for Single-Channel Data
5(9)
1.3 Measures of Robustness
14(13)
1.4 Concluding Remarks
27(1)
2 Robust Estimation: The Linear Regression Model
28(46)
2.1 Complex Derivatives and Optimization
28(3)
2.2 The Linear Model and Organization of the
Chapter
31(2)
2.3 The Least Squares Estimator
33(3)
2.4 Least Absolute Deviation and Rank-Least Absolute Deviation Regression
36(10)
2.5 ML- and A/-estimates of Regression with an Auxiliary Scale Estimate
46(7)
2.6 Joint M-estimation of Regression and Scale Using Huber's Criterion
53(8)
2.7 Measures of Robustness
61(3)
2.8 Positive Breakdown Point Regression Estimators
64(3)
2.9 Simulation Studies
67(5)
2.10 Concluding Remarks
72(2)
3 Robust Penalized Regression in the Linear Model
74(26)
3.1 Sparse Regression and Outline of the
Chapter
74(2)
3.2 Extensions of the Lasso Penalty
76(1)
3.3 The Lasso and the Elastic Net
77(8)
3.4 The Least Absolute Deviation-Lasso and the Rank-Lasso
85(6)
3.5 Joint Penalized A/-estimation of Regression and Scale
91(4)
3.6 Penalty Parameter Selection
95(1)
3.7 Application Example: Prostate Cancer
96(2)
3.8 Concluding Remarks
98(2)
4 Robust Estimation of Location and Scatter (Covariance) Matrix
100(25)
4.1 Complex Vector Space Isomorphism and Complex Distributions
101(2)
4.2 Elliptically Symmetric Distributions
103(6)
4.3 ML- and Af-estimation of the Scatter Matrix
109(3)
4.4 Examples of M- and ML-estimators
112(5)
4.5 Regularized M-estimators of the Scatter Matrix
117(2)
4.6 Signal Detection Application
119(4)
4.7 Concluding Remarks
123(2)
5 Robustness in Sensor Array Processing
125(22)
5.1 Introduction
125(2)
5.2 Basic Array Signal Model
127(4)
5.3 Uncertainties in the Array Signal Model
131(4)
5.4 Statistically Robust Methods
135(7)
5.5 Array Processing Examples
142(4)
5.6 Concluding Remarks
146(1)
6 Tensor Models and Robust Statistics
147(16)
6.1 Introduction
147(1)
6.2 Tensor Notation and Basic Operations
148(5)
6.3 Tensor Decompositions
153(2)
6.4 Robust Tensor Decomposition
155(2)
6.5 Combining Robustness with Sparsity
157(3)
6.6 Simulation Examples
160(2)
6.7 Concluding Remarks
162(1)
7 Robust Filtering
163(23)
7.1 Robust Wiener Filtering
163(3)
7.2 Nonparametric Nonlinear Robust Filters
166(4)
7.3 Robust Kalman Filtering
170(7)
7.4 Robust Extended Kalman Filtering
177(8)
7.5 Concluding Remarks
185(1)
8 Robust Methods for Dependent Data
186(31)
8.1 Signal and Outlier Models
186(4)
8.2 Propagation of Outliers
190(6)
8.3 An Overview of Robust Autoregressive Moving-Average Parameter Estimators
196(5)
8.4 Robust Model Order Selection
201(3)
8.5 Measures of Robustness
204(7)
8.6 Algorithms
211(5)
8.7 Concluding Remarks
216(1)
9 Robust Spectral Estimation
217(16)
9.1 Robust Nonparametric Spectral Estimation
217(7)
9.2 Autoregressive Moving-Average Model-Based Robust Parametric Spectral Estimation
224(2)
9.3 Simulation Example: Robust Spectral Estimation
226(1)
9.4 Robust Subspace-Based Frequency Estimation
227(5)
9.5 Concluding Remarks
232(1)
10 Robust Bootstrap Methods
233(25)
10.1 Introduction
233(2)
10.2 Existing Robust Bootstrap Methods
235(6)
10.3 Robust Bootstrap Confidence Interval Estimation in Linear Regression
241(4)
10.4 Robust and Scalable Bootstrap for Large-Scale Data
245(12)
10.5 Concluding Remarks
257(1)
11 Real-Life Applications
258(14)
11.1 Localization of User Equipment in an Indoor Environment
258(2)
11.2 Blood Glucose Concentration in Photometric Handheld Devices
260(2)
11.3 European Tracer Experiment Source Estimation
262(4)
11.4 Robust Short-Term Load Forecasting
266(2)
11.5 Robust Data Cleaning for Photoplethysmography-Based Pulse-Rate Variability Analysis
268(4)
Bibliography 272(16)
Index 288
Abdelhak M. Zoubir is a Professor of Signal Processing and the Head of the Signal Processing Group at Technische Universität, Darmstadt, Germany. He is a Fellow of the IEEE, an IEEE Distinguished Lecturer, and the co-author of Bootstrap Techniques for Signal Processing (Cambridge, 2004). Visa Koivunen is a Professor of Signal Processing at Aalto University, Finland. He is also a Fellow of the IEEE and an IEEE Distinguished Lecturer. Esa Ollila is an Associate Professor of Signal Processing at Aalto University, Finland. Michael Muma is a Postdoctoral Research Fellow in the Signal Processing Group at Technische Universität, Darmstadt, Germany.