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Multivariate Kernel Smoothing and Its Applications

Multivariate Kernel Smoothing and Its Applications

Hardcover

Series: Chapman & Hall/CRC Monographs on Statistics and Applied Prob

Probability & Statistics

ISBN10: 1498763014
ISBN13: 9781498763011
Publisher: Crc Pr Inc
Published: May 8 2018
Pages: 226
Weight: 1.20
Height: 0.80 Width: 6.40 Depth: 9.40
Language: English
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.

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