Multivariate Nonparametric Regression and Visualization

Jussi Klemelä 지음

Wiley-Interscience 2014년 05월 05일 출간

작품소개

책소개

A modern approach to statistical learning andits applications through visualization methodsWith a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generatingmechanisms, the book begins with an overview of classification and regression.The book then introduces and examines various tested and proven visualization techniques for learning samples and functions. Multivariate Nonparametric Regression and Visualization identifies risk management, portfolio selection, and option pricing as the main areas in which statistical methods may be implemented in quantitative finance. The book provides coverage of key statistical areas including linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods. Exploring the additional applications of nonparametric and semiparametric methods, Multivariate Nonparametric Regression and Visualization features:An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and researchMultiple examples to demonstrate the applications in the field of financeSections with formal definitions of the various applied methods for readers to utilize throughout the bookMultivariate Nonparametric Regression and Visualization is an ideal textbook for upper-undergraduate and graduate-level courses on nonparametric function estimation, advanced topics in statistics, and quantitative finance. The book is also an excellent reference for practitioners who apply statistical methods in quantitative finance.

목차

Preface xviiIntroduction xixI.1 Estimation of Functionals of Conditional Distributions xxI.2 Quantitative Finance xxiI.3 Visualization xxiI.4 Literature xxiiiPART I METHODS OF REGRESSION AND CLASSIFICATION1 Overview of Regression and Classification 31.1 Regression 31.2 Discrete Response Variable 291.3 Parametric Family Regression 331.4 Classification 371.5 Applications in Quantitative Finance 421.6 Data Examples 521.7 Data Transformations 531.8 Central Limit Theorems 581.9 Measuring the Performance of Estimators 611.10 Confidence Sets 731.11 Testing 752 Linear Methods and Extensions 772.1 Linear Regression 782.2 Varying Coefficient Linear Regression 972.3 Generalized Linear and Related Models 1022.4 Series Estimators 1072.5 Conditional Variance and ARCH models 1112.6 Applications in Volatility and Quantile Estimation 1152.7 Linear Classifiers 1243 Kernel Methods and Extensions 1273.1 Regressogram 1293.2 Kernel Estimator 1303.3 Nearest Neighborhood Estimator 1473.4 Classification with Local Averaging 1483.5 Median Smoothing 1513.6 Conditional Density Estimators 1523.7 Conditional Distribution Function Estimation 1583.8 Conditional Quantile Estimation 1603.9 Conditional Variance Estimation 1623.10 Conditional Covariance Estimation 1763.11 Applications in Risk Management 1813.12 Applications in Portfolio Selection 2054 Semiparametric and Structural Models 2294.1 Single Index Model 2304.2 Additive Model 2344.3 Other Semiparametric Models 2375 Empirical Risk Minimization 2415.1 Empirical Risk 2435.2 Local Empirical Risk 2475.3 Support Vector Machines 2575.4 Stagewise Methods 2595.5 Adaptive

작가정보

저자(글) Jussi Klemelä

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JUSSI KLEMEL#x00C4;, PhD, is Senior Research Fellow in the Department of Mathematical Sciences at the University of Oulu. He has written numerous journal articles on his research interests, which include density estimation and the implementation of cutting edge visualization tools. Dr. Klemel#x00E4; is the author of Smoothing of Multivariate Data: Density Estimation and Visualization, also published by Wiley.