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

By: Klemelä, Jussi.
Material type: TextTextSeries: eBooks on Demand.Wiley Series in Computational Statistics: Publisher: Hoboken : Wiley, 2014Description: 1 online resource (668 p.).ISBN: 9781118593509.Subject(s): COMPUTERS / Programming Languages / Visual BASIC | Finance -- Mathematical models | MATHEMATICS / Probability & Statistics / General | MATHEMATICS / Probability & Statistics / Regression Analysis | Regression analysis | VisualizationGenre/Form: Electronic books.Additional physical formats: Print version:: Multivariate Nonparametric Regression and Visualization : With R and Applications to FinanceDDC classification: 519.5 | 519.5/36 Online resources: Click here to view this ebook.
Contents:
Cover; 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 TestingChapter 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 Estimation3.9 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 Reduction6.4 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
Summary: A modern approach to statistical learning and its applications through visualization methods With 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 functio
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
HG176.5 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=1686557 Available EBL1686557

Cover; 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 TestingChapter 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 Estimation3.9 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 Reduction6.4 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

A modern approach to statistical learning and its applications through visualization methods With 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 functio

Description based upon print version of record.

Author notes provided by Syndetics

<p> JUSSI KLEMELÄ, 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. Klemelä is the author of Smoothing of Multivariate Data: Density Estimation and Visualization , also published by Wiley.</p>

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