{"product_id":"financial-signal-processing-and-machine-learning-hardback-9781118745670","title":"Financial Signal Processing and Machine Learning (Hardback) 9781118745670","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eFinancial Signal Processing and Machine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAli N. Akansu (Edited by), A Akansu (Author), Sanjeev R. Kulkarni (Edited by), Dmitry M. Malioutov (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781118745670, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 27 May 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e320 pages\u003cbr\u003e24.6 x 17.3 x 2.3 cm, 0.658 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThe modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. \u003ci\u003eFinancial Signal Processing and Machine Learning\u003c\/i\u003e unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. \u003cp\u003eKey features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eHighlights signal processing and machine learning as key approaches to quantitative finance.\u003c\/li\u003e \u003cli\u003eOffers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems.\u003c\/li\u003e \u003cli\u003ePresents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques.\u003c\/li\u003e \u003cli\u003eIncludes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.\u003c\/li\u003e \u003c\/ul\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eList of Contributors xiii\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Overview 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAli N. Akansu, Sanjeev R. Kulkarni, and Dmitry Malioutov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 A Bird’s-Eye View of Finance 2\u003c\/p\u003e \u003cp\u003e1.2.1 Trading and Exchanges 4\u003c\/p\u003e \u003cp\u003e1.2.2 Technical Themes in the Book 5\u003c\/p\u003e \u003cp\u003e1.3 Overview of the Chapters 6\u003c\/p\u003e \u003cp\u003e1.3.1 Chapter 2: “Sparse Markowitz Portfolios” by \u003ci\u003eChristine De Mol\u003c\/i\u003e 6\u003c\/p\u003e \u003cp\u003e1.3.2 Chapter 3: “Mean-Reverting Portfolios: Tradeoffs between Sparsity and Volatility” by \u003ci\u003eMarco Cuturi\u003c\/i\u003e and \u003ci\u003eAlexandre\u003c\/i\u003e d’Aspremont 7\u003c\/p\u003e \u003cp\u003e1.3.3 Chapter 4: “Temporal Causal Modeling” by \u003ci\u003ePrabhanjan Kambadur, Aurélie C. Lozano, and Ronny\u003c\/i\u003e \u003ci\u003eLuss\u003c\/i\u003e 7\u003c\/p\u003e \u003cp\u003e1.3.4 Chapter 5: “Explicit Kernel and Sparsity of Eigen Subspace for the AR(1) Process” by \u003ci\u003eMustafa U.\u003c\/i\u003e \u003ci\u003eTorun, Onur Yilmaz and Ali N. Akansu\u003c\/i\u003e 7\u003c\/p\u003e \u003cp\u003e1.3.5 Chapter 6: “Approaches to High-Dimensional Covariance and Precision Matrix Estimation” by \u003ci\u003eJianqing Fan, Yuan Liao, and Han Liu\u003c\/i\u003e 7\u003c\/p\u003e \u003cp\u003e1.3.6 Chapter 7: “Stochastic Volatility: Modeling and Asymptotic Approaches to Option Pricing and Portfolio Selection” by \u003ci\u003eMatthew Lorig and Ronnie Sircar\u003c\/i\u003e 7\u003c\/p\u003e \u003cp\u003e1.3.7 Chapter 8: “Statistical Measures of Dependence for Financial Data” by \u003ci\u003eDavid S. Matteson, Nicholas\u003c\/i\u003e \u003ci\u003eA. James, and William B. Nicholson\u003c\/i\u003e 8\u003c\/p\u003e \u003cp\u003e1.3.8 Chapter 9: “Correlated Poisson Processes and Their Applications in Financial Modeling” by \u003ci\u003eAlexander Kreinin\u003c\/i\u003e 8\u003c\/p\u003e \u003cp\u003e1.3.9 Chapter 10: “CVaR Minimizations in Support Vector Machines” by \u003ci\u003eJunya Gotoh and Akiko Takeda\u003c\/i\u003e 8\u003c\/p\u003e \u003cp\u003e1.3.10 Chapter 11: “Regression Models in Risk Management” by \u003ci\u003eStan Uryasev\u003c\/i\u003e 8\u003c\/p\u003e \u003cp\u003e1.4 Other Topics in Financial Signal Processing and Machine Learning 9\u003c\/p\u003e \u003cp\u003eReferences 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Sparse Markowitz Portfolios 11\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChristineDeMol\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Markowitz Portfolios 11\u003c\/p\u003e \u003cp\u003e2.2 Portfolio Optimization as an Inverse Problem: The Need for Regularization 13\u003c\/p\u003e \u003cp\u003e2.3 Sparse Portfolios 15\u003c\/p\u003e \u003cp\u003e2.4 Empirical Validation 17\u003c\/p\u003e \u003cp\u003e2.5 Variations on the Theme 18\u003c\/p\u003e \u003cp\u003e2.5.1 Portfolio Rebalancing 18\u003c\/p\u003e \u003cp\u003e2.5.2 Portfolio Replication or Index Tracking 19\u003c\/p\u003e \u003cp\u003e2.5.3 Other Penalties and Portfolio Norms 19\u003c\/p\u003e \u003cp\u003e2.6 Optimal Forecast Combination 20\u003c\/p\u003e \u003cp\u003eAcknowlegments 21\u003c\/p\u003e \u003cp\u003eReferences 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Mean-Reverting Portfolios 23\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMarco Cuturi and Alexandre d’Aspremont\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 23\u003c\/p\u003e \u003cp\u003e3.1.1 Synthetic Mean-Reverting Baskets 24\u003c\/p\u003e \u003cp\u003e3.1.2 Mean-Reverting Baskets with Sufficient Volatility and Sparsity 24\u003c\/p\u003e \u003cp\u003e3.2 Proxies for Mean Reversion 25\u003c\/p\u003e \u003cp\u003e3.2.1 Related Work and Problem Setting 25\u003c\/p\u003e \u003cp\u003e3.2.2 Predictability 26\u003c\/p\u003e \u003cp\u003e3.2.3 Portmanteau Criterion 27\u003c\/p\u003e \u003cp\u003e3.2.4 Crossing Statistics 28\u003c\/p\u003e \u003cp\u003e3.3 Optimal Baskets 28\u003c\/p\u003e \u003cp\u003e3.3.1 Minimizing Predictability 29\u003c\/p\u003e \u003cp\u003e3.3.2 Minimizing the Portmanteau Statistic 29\u003c\/p\u003e \u003cp\u003e3.3.3 Minimizing the Crossing Statistic 29\u003c\/p\u003e \u003cp\u003e3.4 Semidefinite Relaxations and Sparse Components 30\u003c\/p\u003e \u003cp\u003e3.4.1 A Semidefinite Programming Approach to Basket Estimation 30\u003c\/p\u003e \u003cp\u003e3.4.2 Predictability 30\u003c\/p\u003e \u003cp\u003e3.4.3 Portmanteau 31\u003c\/p\u003e \u003cp\u003e3.4.4 Crossing Stats 31\u003c\/p\u003e \u003cp\u003e3.5 Numerical Experiments 32\u003c\/p\u003e \u003cp\u003e3.5.1 Historical Data 32\u003c\/p\u003e \u003cp\u003e3.5.2 Mean-reverting Basket Estimators 33\u003c\/p\u003e \u003cp\u003e3.5.3 Jurek and Yang (2007) Trading Strategy 33\u003c\/p\u003e \u003cp\u003e3.5.4 Transaction Costs 33\u003c\/p\u003e \u003cp\u003e3.5.5 Experimental Setup 36\u003c\/p\u003e \u003cp\u003e3.5.6 Results 36\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 39\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Temporal Causal Modeling 41\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePrabhanjan Kambadur, Aurélie C. Lozano, and Ronny Luss\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 41\u003c\/p\u003e \u003cp\u003e4.2 TCM 46\u003c\/p\u003e \u003cp\u003e4.2.1 Granger Causality and Temporal Causal Modeling 46\u003c\/p\u003e \u003cp\u003e4.2.2 Grouped Temporal Causal Modeling Method 47\u003c\/p\u003e \u003cp\u003e4.2.3 Synthetic Experiments 49\u003c\/p\u003e \u003cp\u003e4.3 Causal Strength Modeling 51\u003c\/p\u003e \u003cp\u003e4.4 Quantile TCM (Q-TCM) 52\u003c\/p\u003e \u003cp\u003e4.4.1 Modifying Group OMP for Quantile Loss 52\u003c\/p\u003e \u003cp\u003e4.4.2 Experiments 53\u003c\/p\u003e \u003cp\u003e4.5 TCM with Regime Change Identification 55\u003c\/p\u003e \u003cp\u003e4.5.1 Model 56\u003c\/p\u003e \u003cp\u003e4.5.2 Algorithm 58\u003c\/p\u003e \u003cp\u003e4.5.3 Synthetic Experiments 60\u003c\/p\u003e \u003cp\u003e4.5.4 Application: Analyzing Stock Returns 62\u003c\/p\u003e \u003cp\u003e4.6 Conclusions 63\u003c\/p\u003e \u003cp\u003eReferences 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Explicit Kernel and Sparsity of Eigen Subspace for the AR(1) Process 67\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMustafa U. Torun, Onur Yilmaz, and Ali N. Akansu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 67\u003c\/p\u003e \u003cp\u003e5.2 Mathematical Definitions 68\u003c\/p\u003e \u003cp\u003e5.2.1 Discrete AR(1) Stochastic Signal Model 68\u003c\/p\u003e \u003cp\u003e5.2.2 Orthogonal Subspace 69\u003c\/p\u003e \u003cp\u003e5.3 Derivation of Explicit KLT Kernel for a Discrete AR(1) Process 72\u003c\/p\u003e \u003cp\u003e5.3.1 A Simple Method for Explicit Solution of a Transcendental Equation 73\u003c\/p\u003e \u003cp\u003e5.3.2 Continuous Process with Exponential Autocorrelation 74\u003c\/p\u003e \u003cp\u003e5.3.3 Eigenanalysis of a Discrete AR(1) Process 76\u003c\/p\u003e \u003cp\u003e5.3.4 Fast Derivation of KLT Kernel for an AR(1) Process 79\u003c\/p\u003e \u003cp\u003e5.4 Sparsity of Eigen Subspace 82\u003c\/p\u003e \u003cp\u003e5.4.1 Overview of Sparsity Methods 83\u003c\/p\u003e \u003cp\u003e5.4.2 pdf-Optimized Midtread Quantizer 84\u003c\/p\u003e \u003cp\u003e5.4.3 Quantization of Eigen Subspace 86\u003c\/p\u003e \u003cp\u003e5.4.4 pdf of Eigenvector 87\u003c\/p\u003e \u003cp\u003e5.4.5 Sparse KLT Method 89\u003c\/p\u003e \u003cp\u003e5.4.6 Sparsity Performance 91\u003c\/p\u003e \u003cp\u003e5.5 Conclusions 97\u003c\/p\u003e \u003cp\u003eReferences 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Approaches to High-Dimensional Covariance and Precision Matrix Estimations 100\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJianqing Fan, Yuan Liao, and Han Liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 100\u003c\/p\u003e \u003cp\u003e6.2 Covariance Estimation via Factor Analysis 101\u003c\/p\u003e \u003cp\u003e6.2.1 Known Factors 103\u003c\/p\u003e \u003cp\u003e6.2.2 Unknown Factors 104\u003c\/p\u003e \u003cp\u003e6.2.3 Choosing the Threshold 105\u003c\/p\u003e \u003cp\u003e6.2.4 Asymptotic Results 105\u003c\/p\u003e \u003cp\u003e6.2.5 A Numerical Illustration 107\u003c\/p\u003e \u003cp\u003e6.3 Precision Matrix Estimation and Graphical Models 109\u003c\/p\u003e \u003cp\u003e6.3.1 Column-wise Precision Matrix Estimation 110\u003c\/p\u003e \u003cp\u003e6.3.2 The Need for Tuning-insensitive Procedures 111\u003c\/p\u003e \u003cp\u003e6.3.3 TIGER: A Tuning-insensitive Approach for Optimal Precision Matrix Estimation 112\u003c\/p\u003e \u003cp\u003e6.3.4 Computation 114\u003c\/p\u003e \u003cp\u003e6.3.5 Theoretical Properties of TIGER 114\u003c\/p\u003e \u003cp\u003e6.3.6 Applications to Modeling Stock Returns 115\u003c\/p\u003e \u003cp\u003e6.3.7 Applications to Genomic Network 118\u003c\/p\u003e \u003cp\u003e6.4 Financial Applications 119\u003c\/p\u003e \u003cp\u003e6.4.1 Estimating Risks of Large Portfolios 119\u003c\/p\u003e \u003cp\u003e6.4.2 Large Panel Test of Factor Pricing Models 121\u003c\/p\u003e \u003cp\u003e6.5 Statistical Inference in Panel Data Models 126\u003c\/p\u003e \u003cp\u003e6.5.1 Efficient Estimation in Pure Factor Models 126\u003c\/p\u003e \u003cp\u003e6.5.2 Panel Data Model with Interactive Effects 127\u003c\/p\u003e \u003cp\u003e6.5.3 Numerical Illustrations 130\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 131\u003c\/p\u003e \u003cp\u003eReferences 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Stochastic Volatility 135\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMatthew Lorig and Ronnie Sircar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 135\u003c\/p\u003e \u003cp\u003e7.1.1 Options and Implied Volatility 136\u003c\/p\u003e \u003cp\u003e7.1.2 Volatility Modeling 137\u003c\/p\u003e \u003cp\u003e7.2 Asymptotic Regimes and Approximations 141\u003c\/p\u003e \u003cp\u003e7.2.1 Contract Asymptotics 142\u003c\/p\u003e \u003cp\u003e7.2.2 Model Asymptotics 142\u003c\/p\u003e \u003cp\u003e7.2.3 Implied Volatility Asymptotics 143\u003c\/p\u003e \u003cp\u003e7.2.4 Tractable Models 145\u003c\/p\u003e \u003cp\u003e7.2.5 Model Coefficient Polynomial Expansions 146\u003c\/p\u003e \u003cp\u003e7.2.6 Small “Vol of Vol” Expansion 152\u003c\/p\u003e \u003cp\u003e7.2.7 Separation of Timescales Approach 152\u003c\/p\u003e \u003cp\u003e7.2.8 Comparison of the Expansion Schemes 154\u003c\/p\u003e \u003cp\u003e7.3 Merton Problem with Stochastic Volatility: Model Coefficient Polynomial Expansions 155\u003c\/p\u003e \u003cp\u003e7.3.1 Models and Dynamic Programming Equation 155\u003c\/p\u003e \u003cp\u003e7.3.2 Asymptotic Approximation 157\u003c\/p\u003e \u003cp\u003e7.3.3 Power Utility 159\u003c\/p\u003e \u003cp\u003e7.4 Conclusions 160\u003c\/p\u003e \u003cp\u003eAcknowledgements 160\u003c\/p\u003e \u003cp\u003eReferences 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Statistical Measures of Dependence for Financial Data 162\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDavid S. Matteson, Nicholas A. James, and William B. Nicholson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 162\u003c\/p\u003e \u003cp\u003e8.2 Robust Measures of Correlation and Autocorrelation 164\u003c\/p\u003e \u003cp\u003e8.2.1 Transformations and Rank-Based Methods 166\u003c\/p\u003e \u003cp\u003e8.2.2 Inference 169\u003c\/p\u003e \u003cp\u003e8.2.3 Misspecification Testing 171\u003c\/p\u003e \u003cp\u003e8.3 Multivariate Extensions 174\u003c\/p\u003e \u003cp\u003e8.3.1 Multivariate Volatility 175\u003c\/p\u003e \u003cp\u003e8.3.2 Multivariate Misspecification Testing 176\u003c\/p\u003e \u003cp\u003e8.3.3 Granger Causality 176\u003c\/p\u003e \u003cp\u003e8.3.4 Nonlinear Granger Causality 177\u003c\/p\u003e \u003cp\u003e8.4 Copulas 179\u003c\/p\u003e \u003cp\u003e8.4.1 Fitting Copula Models 180\u003c\/p\u003e \u003cp\u003e8.4.2 Parametric Copulas 181\u003c\/p\u003e \u003cp\u003e8.4.3 Extending beyond Two Random Variables 183\u003c\/p\u003e \u003cp\u003e8.4.4 Software 185\u003c\/p\u003e \u003cp\u003e8.5 Types of Dependence 185\u003c\/p\u003e \u003cp\u003e8.5.1 Positive and Negative Dependence 185\u003c\/p\u003e \u003cp\u003e8.5.2 Tail Dependence 187\u003c\/p\u003e \u003cp\u003eReferences 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Correlated Poisson Processes and Their Applications in Financial Modeling 191\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAlexander Kreinin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 191\u003c\/p\u003e \u003cp\u003e9.2 Poisson Processes and Financial Scenarios 193\u003c\/p\u003e \u003cp\u003e9.2.1 Integrated Market–Credit Risk Modeling 193\u003c\/p\u003e \u003cp\u003e9.2.2 Market Risk and Derivatives Pricing 194\u003c\/p\u003e \u003cp\u003e9.2.3 Operational Risk Modeling 194\u003c\/p\u003e \u003cp\u003e9.2.4 Correlation of Operational Events 195\u003c\/p\u003e \u003cp\u003e9.3 Common Shock Model and Randomization of Intensities 196\u003c\/p\u003e \u003cp\u003e9.3.1 Common Shock Model 196\u003c\/p\u003e \u003cp\u003e9.3.2 Randomization of Intensities 196\u003c\/p\u003e \u003cp\u003e9.4 Simulation of Poisson Processes 197\u003c\/p\u003e \u003cp\u003e9.4.1 Forward Simulation 197\u003c\/p\u003e \u003cp\u003e9.4.2 Backward Simulation 200\u003c\/p\u003e \u003cp\u003e9.5 Extreme Joint Distribution 207\u003c\/p\u003e \u003cp\u003e9.5.1 Reduction to Optimization Problem 207\u003c\/p\u003e \u003cp\u003e9.5.2 Monotone Distributions 208\u003c\/p\u003e \u003cp\u003e9.5.3 Computation of the Joint Distribution 214\u003c\/p\u003e \u003cp\u003e9.5.4 On the Frechet–Hoeffding Theorem 215\u003c\/p\u003e \u003cp\u003e9.5.5 Approximation of the Extreme Distributions 217\u003c\/p\u003e \u003cp\u003e9.6 Numerical Results 219\u003c\/p\u003e \u003cp\u003e9.6.1 Examples of the Support 219\u003c\/p\u003e \u003cp\u003e9.6.2 Correlation Boundaries 221\u003c\/p\u003e \u003cp\u003e9.7 Backward Simulation of the Poisson-Wiener Process 222\u003c\/p\u003e \u003cp\u003e9.8 Concluding Remarks 227\u003c\/p\u003e \u003cp\u003eAcknowledgments 228\u003c\/p\u003e \u003cp\u003eAppendix A 229\u003c\/p\u003e \u003cp\u003eA. 1 Proof of Lemmas 9.2 and 9.3 229\u003c\/p\u003e \u003cp\u003eA.1.1 Proof of Lemma 9.2 229\u003c\/p\u003e \u003cp\u003eA.1.2 Proof of Lemma 9.3 230\u003c\/p\u003e \u003cp\u003eReferences 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 CVaR Minimizations in Support Vector Machines 233\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJun-ya Gotoh and Akiko Takeda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 What Is CVaR? 234\u003c\/p\u003e \u003cp\u003e10.1.1 Definition and Interpretations 234\u003c\/p\u003e \u003cp\u003e10.1.2 Basic Properties of CVaR 238\u003c\/p\u003e \u003cp\u003e10.1.3 Minimization of CVaR 240\u003c\/p\u003e \u003cp\u003e10.2 Support Vector Machines 242\u003c\/p\u003e \u003cp\u003e10.2.1 Classification 242\u003c\/p\u003e \u003cp\u003e10.2.2 Regression 246\u003c\/p\u003e \u003cp\u003e10.3 ν-SVMs as CVaR Minimizations 247\u003c\/p\u003e \u003cp\u003e10.3.1 ν-SVMs as CVaR Minimizations with Homogeneous Loss 247\u003c\/p\u003e \u003cp\u003e10.3.2 ν-SVMs as CVaR Minimizations with Nonhomogeneous Loss 251\u003c\/p\u003e \u003cp\u003e10.3.3 Refining the ν-Property 253\u003c\/p\u003e \u003cp\u003e10.4 Duality 256\u003c\/p\u003e \u003cp\u003e10.4.1 Binary Classification 256\u003c\/p\u003e \u003cp\u003e10.4.2 Geometric Interpretation of ν-SVM 257\u003c\/p\u003e \u003cp\u003e10.4.3 Geometric Interpretation of the Range of ν for ν-SVC 258\u003c\/p\u003e \u003cp\u003e10.4.4 Regression 259\u003c\/p\u003e \u003cp\u003e10.4.5 One-class Classification and SVDD 259\u003c\/p\u003e \u003cp\u003e10.5 Extensions to Robust Optimization Modelings 259\u003c\/p\u003e \u003cp\u003e10.5.1 Distributionally Robust Formulation 259\u003c\/p\u003e \u003cp\u003e10.5.2 Measurement-wise Robust Formulation 261\u003c\/p\u003e \u003cp\u003e10.6 Literature Review 262\u003c\/p\u003e \u003cp\u003e10.6.1 CVaR as a Risk Measure 263\u003c\/p\u003e \u003cp\u003e10.6.2 From CVaR Minimization to SVM 263\u003c\/p\u003e \u003cp\u003e10.6.3 From SVM to CVaR Minimization 263\u003c\/p\u003e \u003cp\u003e10.6.4 Beyond CVaR 263\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Regression Models in Risk Management 266\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eStan Uryasev\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 267\u003c\/p\u003e \u003cp\u003e11.2 Error and Deviation Measures 268\u003c\/p\u003e \u003cp\u003e11.3 Risk Envelopes and Risk Identifiers 271\u003c\/p\u003e \u003cp\u003e11.3.1 Examples of Deviation Measures D, Corresponding Risk Envelopes Q, and Sets of Risk Identifiers QD(X) 272\u003c\/p\u003e \u003cp\u003e11.4 Error Decomposition in Regression 273\u003c\/p\u003e \u003cp\u003e11.5 Least-Squares Linear Regression 275\u003c\/p\u003e \u003cp\u003e11.6 Median Regression 277\u003c\/p\u003e \u003cp\u003e11.7 Quantile Regression and Mixed Quantile Regression 281\u003c\/p\u003e \u003cp\u003e11.8 Special Types of Linear Regression 283\u003c\/p\u003e \u003cp\u003e11.9 Robust Regression 284\u003c\/p\u003e \u003cp\u003eReferences, Further Reading, and Bibliography 287\u003c\/p\u003e \u003cp\u003eIndex 289\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Brand New","offer_id":52173803847960,"sku":"9781118745670","price":82.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781118745670.jpg?v=1781172595","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/financial-signal-processing-and-machine-learning-hardback-9781118745670","provider":"Freshly Printed Books","version":"1.0","type":"link"}