Multivariate Nonparametric Regression and Visualization : With R and Applications to Finance.

Intro -- Half Title page -- Title page -- Copyright page -- Dedication -- Preface -- Introduction -- I.1 Estimation of Functionals of Conditional Distributions -- I.2 Quantitative Finance -- I.3 Visualization -- I.4 Literature -- Part I: Methods of Regression and Classification -- Chapter 1: Overview of Regression and Classification -- 1.1 Regression -- 1.2 Discrete Response Variable -- 1.3 Parametric Family Regression -- 1.4 Classification -- 1.5 Applications in Quantitative Finance -- 1.6 Data Examples -- 1.7 Data Transformations -- 1.8 Central Limit Theorems -- 1.9 Measuring the Performance of Estimators -- 1.10 Confidence Sets -- 1.11 Testing -- Chapter 2: Linear Methods and Extensions -- 2.1 Linear Regression -- 2.2 Varying Coefficient Linear Regression -- 2.3 Generalized Linear and Related Models -- 2.4 Series Estimators -- 2.5 Conditional Variance and ARCH Models -- 2.6 Applications in Volatility and Quantile Estimation -- 2.7 Linear Classifiers -- Chapter 3: Kernel Methods and Extensions -- 3.1 Regressogram -- 3.2 Kernel Estimator -- 3.3 Nearest-Neighbor Estimator -- 3.4 Classification with Local Averaging -- 3.5 Median Smoothing -- 3.6 Conditional Density Estimation -- 3.7 Conditional Distribution Function Estimation -- 3.8 Conditional Quantile Estimation -- 3.9 Conditional Variance Estimation -- 3.10 Conditional Covariance Estimation -- 3.11 Applications in Risk Management -- 3.12 Applications in Portfolio Selection -- Chapter 4: Semiparametric and Structural Models -- 4.1 Single-Index Model -- 4.2 Additive Model -- 4.3 Other Semiparametric Models -- Chapter 5: Empirical Risk Minimization -- 5.1 Empirical Risk -- 5.3 Support Vector Machines -- 5.4 Stagewise Methods -- 5.5 Adaptive Regressograms -- Part II: Visualization -- Chapter 6: Visualization of Data -- 6.1 Scatter Plots -- 6.2 Histogram and Kernel Density Estimator.

6.3 Dimension Reduction -- 6.4 Observations as Objects -- Chapter 7: Visualization of Functions -- 7.1 Slices -- 7.2 Partial Dependence Functions -- 7.3 Reconstruction of Sets -- 7.4 Level Set Trees -- 7.5 Unimodal Densities -- Appendix A: R Tutorial -- A.1 Data Visualization -- A.2 Linear Regression -- A.3 Kernel Regression -- A.4 Local Linear Regression -- A.5 Additive Models: Backfitting -- A.6 Single-Index Regression -- A.7 Forward Stagewise Modeling -- A.8 Quantile Regression -- References -- Author Index -- Topic Index.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.