Kernel smoothing refers to a general methodology for recovery of the underlying structure in data sets without the imposition of a parametric model. The main goal of this book is to develop the reader’s intuition and mathematical skills required for a comprehensive understanding of kernel smoothing, and hence smoothing problems in general. To describe the principles, applications and analysis of kernel smoothers the authors concentrate on the simplest nonparametric curve estimation setting, namely density and regression estimation. Special attention is given to the problem of choosing the smoothing parameter.par For the study of the book only a basic knowledge of statistics, calculus and matrix algebra is assumed. In its role as an introductory text this book does make some sacrifices. It does not completely cover the vast amount of research in the field of kernel smoothing. But the bibliographical notes at the end of each chapter provide a comprehensive, up-to-date reference for those readers which are more familiar with the topic. (Source:

References in zbMATH (referenced in 937 articles , 1 standard article )

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  9. Hu, Shengwei; Wang, Yong: Modal clustering using semiparametric mixtures and mode flattening (2021)
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  12. Luati, Alessandra; Novelli, Marco: Explicit-duration hidden Markov models for quantum state estimation (2021)
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  14. Rattihalli, R. N.; Patil, S. B.: Data dependent asymmetric kernels for estimating the density function (2021)
  15. Rodríguez-Berrio, Felipe; Rodríguez-Cortés, Francisco J.; Mateu, Jorge; Adelfio, Giada: On some statistical properties of the spatio-temporal product density (2021)
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  18. Arsalane Chouaib Guidoum: Kernel Estimator and Bandwidth Selection for Density and its Derivatives: The kedd Package (2020) arXiv
  19. Bendich, Paul; Bubenik, Peter; Wagner, Alexander: Stabilizing the unstable output of persistent homology computations (2020)
  20. Beran, Jan; Telkmann, Klaus: On nonparametric ridge estimation for multivariate long-memory processes (2020)

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