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Sucharita Ghosh

Kernel Smoothing

Principles, Methods and Applications

Ebook (EPUB Format)

Comprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of contexts, considering independent and correlated data, as well as non-Gaussian data that are transformations of latent Gaussian processes. Topics including nonparametric density estimation, nonparametric and semiparametric regression, and trend and surface estimation in particular for time series and spatial data are introduc… Mehr

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Produktdetails


  • ISBN: 978-1-118-89051-6
  • EAN: 9781118890516
  • Produktnummer: 24830711
  • Verlag: Wiley
  • Sprache: Englisch
  • Erscheinungsjahr: 2017
  • Seitenangabe: 272 S.
  • Plattform: EPUB
  • Masse: 5'600 KB

Über den Autor


Sucharita Ghosh, PhD, is a statistician at the Swiss Federal Research Institute WSL, Switzerland. She also teaches graduate level Statistics in the Department of Mathematics, Swiss Federal Institute of Technology in Zurich. She obtained her doctorate in Statistics from the University of Toronto, Masters from the Indian Statistical Institute and B.Sc. from Presidency College, University of Calcutta, India. She was a Statistics faculty member at Cornell University and has held various short-term and long-term visiting faculty positions at universities such as the University of North Carolina at Chapel Hill and University of York, UK. She has also taught Statistics to undergraduate and graduate students at a number of universities, namely in Canada (Toronto), USA (Cornell, UNC Chapel Hill), UK (York), Germany (Konstanz) and Switzerland (ETH Zurich). Her research interests include smoothing, integral transforms, time series and spatial data analysis, having applications in a number of areas including the natural sciences, finance and medicine among others.

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