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 generating mechanisms, 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
research
- Multiple examples to demonstrate the applications in the field of
finance
- Sections with formal definitions of the various applied methods for
readers to utilize throughout the book
Multivariate 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.
