Multivariate Kernel Smoothing and Its Applications

José E. Chacón, Tarn Duong

May 8, 2018 by Chapman and Hall/CRC
Reference - 226 Pages
ISBN 9781498763011 - CAT# K28946
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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Features

gathers together some recent advances in multivariate kernel smoothing, and its applications related to novel statistical analysis techniques for data sets from current research topics

provides an overview of the state of the art of data smoothing, by providing a solid theoretical context for empirical data analysis.

presents data smoothing as a tool for Exploratory Data Analysis, even for low-to-moderate-dimensional data, as a first step before any inference or regression fitting.

Summary

Kernel smoothing has greatly evolved since its inception to become an essential methodology in the Data Science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solutions to complex data challenges.

Multivariate Kernel Smoothing and Its Applications offers a comprehensive overview of both aspects. It begins with a thorough exposition of the approaches to achieve the two basic goals of estimating probability density functions and their derivatives. The focus then turns to the applications of these approaches to more complex data analysis goals, many with a geometric/topological flavour, such as level set estimation, clustering (unsupervised learning), principal curves, and feature significance. Other topics, while not direct applications of density (derivative) estimation but sharing many commonalities with the previous settings, include classification (supervised learning), nearest neighbour estimation, and deconvolution for data observed with error.

For a Data Scientist, each chapter contains illustrative Open Data examples that are analysed by the most appropriate kernel smoothing method. The emphasis is always placed on an intuitive understanding of the data provided by the accompanying statistical visualisations. For a reader wishing to investigate further the details of their underlying statistical reasoning, a graduated exposition to a unified theoretical framework is provided. The algorithms for efficient software implementation are also discussed.

José E. Chacón is an associate professor at the Department of Mathematics of the Universidad de Extremadura in Spain. Tarn Duong is a Senior Data Scientist for a start- up which provides short distance carpooling services in France. Both authors have made important contributions to kernel smoothing research over the last couple of decades.
Tarn Duong is a Senior Data Scientist for a start-up which provides short distance carpooling services in France.

Both authors have made important contributions to kernel smoothing research over the last couple of decades.

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