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 930 articles , 1 standard article )

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  1. Ben Abdellah, Amal; L’Ecuyer, Pierre; Owen, Art B.; Puchhammer, Florian: Density estimation by randomized quasi-Monte Carlo (2021)
  2. Borisov, Igor S.; Linke, Yuliana Yu.; Ruzankin, Pavel S.: Universal weighted kernel-type estimators for some class of regression models (2021)
  3. Cui, Qiurong; Xu, Yuqing; Zhang, Zhengjun; Chan, Vincent: Max-linear regression models with regularization (2021)
  4. Deng, Changbao; Jiang, Weinuo; Wang, Shihong: Detecting interactions in discrete-time dynamics by random variable resetting (2021)
  5. Fiebig, Ewelina Marta: On data-driven choice of (\lambda) in nonparametric Gaussian regression via propagation-separation approach (2021)
  6. Kirkby, J. Lars; Leitao, Álvaro; Nguyen, Duy: Nonparametric density estimation and bandwidth selection with B-spline bases: a novel Galerkin method (2021)
  7. Luati, Alessandra; Novelli, Marco: Explicit-duration hidden Markov models for quantum state estimation (2021)
  8. Markovich, L. A.: Nonparametric estimation of multivariate density and its derivative by dependent data using gamma kernels (2021)
  9. Rattihalli, R. N.; Patil, S. B.: Data dependent asymmetric kernels for estimating the density function (2021)
  10. Akyildiz, Ömer Deniz; Crisan, Dan; Míguez, Joaquín: Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization (2020)
  11. Arsalane Chouaib Guidoum: Kernel Estimator and Bandwidth Selection for Density and its Derivatives: The kedd Package (2020) arXiv
  12. Bendich, Paul; Bubenik, Peter; Wagner, Alexander: Stabilizing the unstable output of persistent homology computations (2020)
  13. Beran, Jan; Telkmann, Klaus: On nonparametric ridge estimation for multivariate long-memory processes (2020)
  14. Bischofberger, Stephan M.; Hiabu, Munir; Isakson, Alex: Continuous chain-ladder with paid data (2020)
  15. Bouzebda, Salim; El-hadjali, Thouria: Uniform convergence rate of the kernel regression estimator adaptive to intrinsic dimension in presence of censored data (2020)
  16. Braun, W. John; Stafford, James; Brown, Patrick: Data sharpening via Firth’s adjusted score function (2020)
  17. Casa, Alessandro; Chacón, José E.; Menardi, Giovanna: Modal clustering asymptotics with applications to bandwidth selection (2020)
  18. Cattaneo, Matias D.; Jansson, Michael; Ma, Xinwei: Simple local polynomial density estimators (2020)
  19. Crespi, Giovanni Paolo; Mastrogiacomo, Elisa: Qualitative robustness of set-valued value-at-risk (2020)
  20. Cui, Zhenyu; Kirkby, Justin Lars; Nguyen, Duy: Nonparametric density estimation by B-spline duality (2020)

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