Multivariate Nonparametric Regression and Visualization / Wiley Series in Computational Statistics (ePub)

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.