Multivariate density estimation. Theory, practice, and visualization.
2nd ed.

*(English)*Zbl 1311.62004
Wiley Series in Probability and Statistics. Hoboken, NJ: John Wiley & Sons (ISBN 978-0-471-69755-8/hbk; 978-1-118-57557-4/ebook). xviii, 350 p. (2015).

Publisher’s description: Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods

Featuring a thoroughly revised presentation, the book maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the second edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis.

The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the second edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, the book also features:

For the first edition see [Zbl 0850.62006].

Featuring a thoroughly revised presentation, the book maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the second edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis.

The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the second edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, the book also features:

- {\(\bullet\)}
- over 150 updated figures to clarify theoretical results and to show analyses of real data sets
- {\(\bullet\)}
- an updated presentation of graphic visualization using computer software such as R
- {\(\bullet\)}
- a clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering
- {\(\bullet\)}
- more than 130 problems to help readers reinforce the main concepts and ideas presented
- {\(\bullet\)}
- boxed theorems and results allowing easy identification of crucial ideas
- {\(\bullet\)}
- figures in color in the digital versions of the book
- {\(\bullet\)}
- a website with related data sets

For the first edition see [Zbl 0850.62006].

##### MSC:

62-02 | Research exposition (monographs, survey articles) pertaining to statistics |

62G07 | Density estimation |

62H05 | Characterization and structure theory for multivariate probability distributions; copulas |