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 947 articles , 1 standard article )

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  1. Zhou, Xingcai: Uniform convergence rates for wavelet curve estimation in sup-norm loss (2022)
  2. Basulto-Elias, Guillermo; Carriquiry, Alicia L.; De Brabanter, Kris; Nordman, Daniel J.: Bivariate kernel deconvolution with panel data (2021)
  3. Ben Abdellah, Amal; L’Ecuyer, Pierre; Owen, Art B.; Puchhammer, Florian: Density estimation by randomized quasi-Monte Carlo (2021)
  4. Borisov, Igor S.; Linke, Yuliana Yu.; Ruzankin, Pavel S.: Universal weighted kernel-type estimators for some class of regression models (2021)
  5. Bouzebda, Salim; Didi, Sultana: Some asymptotic properties of kernel regression estimators of the mode for stationary and ergodic continuous time processes (2021)
  6. Chakraborty, Anirvan; Panaretos, Victor M.: Functional registration and local variations: identifiability, rank, and tuning (2021)
  7. Christian Thiele; Gerrit Hirschfeld: cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R (2021) not zbMATH
  8. Colbrook, Matthew; Horning, Andrew; Townsend, Alex: Computing spectral measures of self-adjoint operators (2021)
  9. Comte, Fabienne; Marie, Nicolas: On a Nadaraya-Watson estimator with two bandwidths (2021)
  10. Cui, Qiurong; Xu, Yuqing; Zhang, Zhengjun; Chan, Vincent: Max-linear regression models with regularization (2021)
  11. Deng, Changbao; Jiang, Weinuo; Wang, Shihong: Detecting interactions in discrete-time dynamics by random variable resetting (2021)
  12. Faugeras, Olivier P.; Rüschendorf, Ludger: Functional, randomized and smoothed multivariate quantile regions (2021)
  13. Fiebig, Ewelina Marta: On data-driven choice of (\lambda) in nonparametric Gaussian regression via propagation-separation approach (2021)
  14. Geenens, Gery: Mellin-Meijer kernel density estimation on (\mathbbR^+) (2021)
  15. Ghassabeh, Youness Aliyari; Rudzicz, Frank: Modified subspace constrained mean shift algorithm (2021)
  16. Hu, Shengwei; Wang, Yong: Modal clustering using semiparametric mixtures and mode flattening (2021)
  17. Kakizawa, Yoshihide: Recursive asymmetric kernel density estimation for nonnegative data (2021)
  18. Kirkby, J. Lars; Leitao, Álvaro; Nguyen, Duy: Nonparametric density estimation and bandwidth selection with B-spline bases: a novel Galerkin method (2021)
  19. Kounetas, Konstantinos E.; Polemis, Michael L.; Tzeremes, Nickolaos G.: Measurement of eco-efficiency and convergence: evidence from a non-parametric frontier analysis (2021)
  20. Luati, Alessandra; Novelli, Marco: Explicit-duration hidden Markov models for quantum state estimation (2021)

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