Multivariate nonparametric regression and visualization : with R and applications to finance / / Jussi Klemela

• Autore: Klemelä Jussi <1965->
• Pubblicazione: Hoboken, New Jersey : , : Wiley, , 2014; ©2014
• Descrizione fisica: 1 online resource (668 p.)
• Disciplina: 519.5/36
• Soggetto topico: Finance - Mathematical models; Visualization; Regression analysis
• Soggetto genere / forma: Electronic books.
• Classificazione: MAT029000MAT029030COM051200
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: 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
• Sommario/riassunto: "This book uniquely utilizes visualization tools to explain and study statistical learning methods. Covering classification and regression, the book is divided into two parts. First, various visualization methods are introduced and explained. Here, the reader is presented with applications of visualization techniques to learning samples (including projection pursuit, graphical matrices, and parallel coordinate plots) as well as functions, and sets. Next, the author provides a "toolbox" that contains formal definitions of the methods applied in the book and then proceeds to present visualizations of classified learning samples and classified test samples. Visualization methods are applied for the initial exploration of data, to identify the correct type of classifier, and to estimate the best achievable classification error. Once identified, the classifier's properties, proper uses, and overall performance are demonstrated and measured using visualization methods. Key areas of coverage include linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods In addition to providing applications to engineering and biomedicine, the author also uses financial data sets as real data examples to illustrate nonparametric function estimation. The author's own R software is used throughout to reproduce and modify the book's computations and research. Readers can duplicate these applications using the software, available via the book's related Web site"--
• Titolo autorizzato: Multivariate nonparametric regression and visualization
• ISBN: 1-118-83804-1; 1-118-59350-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910464175703321
• Lo trovi qui: Univ. Federico II
• Serie: Wiley series in computational statistics.