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 921 articles , 1 standard article )

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  1. Borisov, Igor S.; Linke, Yuliana Yu.; Ruzankin, Pavel S.: Universal weighted kernel-type estimators for some class of regression models (2021)
  2. Cui, Qiurong; Xu, Yuqing; Zhang, Zhengjun; Chan, Vincent: Max-linear regression models with regularization (2021)
  3. Akyildiz, Ömer Deniz; Crisan, Dan; Míguez, Joaquín: Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization (2020)
  4. Arsalane Chouaib Guidoum: Kernel Estimator and Bandwidth Selection for Density and its Derivatives: The kedd Package (2020) arXiv
  5. Bendich, Paul; Bubenik, Peter; Wagner, Alexander: Stabilizing the unstable output of persistent homology computations (2020)
  6. Bischofberger, Stephan M.; Hiabu, Munir; Isakson, Alex: Continuous chain-ladder with paid data (2020)
  7. Braun, W. John; Stafford, James; Brown, Patrick: Data sharpening via Firth’s adjusted score function (2020)
  8. Casa, Alessandro; Chacón, José E.; Menardi, Giovanna: Modal clustering asymptotics with applications to bandwidth selection (2020)
  9. Cattaneo, Matias D.; Jansson, Michael; Ma, Xinwei: Simple local polynomial density estimators (2020)
  10. Crespi, Giovanni Paolo; Mastrogiacomo, Elisa: Qualitative robustness of set-valued value-at-risk (2020)
  11. Cui, Zhenyu; Kirkby, Justin Lars; Nguyen, Duy: Nonparametric density estimation by B-spline duality (2020)
  12. Deng, Hao; To, Albert C.: Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design (2020)
  13. Ellis, John R.; Petrovskaya, Natalia B.: A computational study of density-dependent individual movement and the formation of population clusters in two-dimensional spatial domains (2020)
  14. Ferraccioli, Federico; Sangalli, Laura M.; Arnone, Eleonora; Finos, Livio: A functional data analysis approach to the estimation of densities over complex regions (2020)
  15. Finazzi, Francesco; Paci, Lucia: Kernel-based estimation of individual location densities from smartphone data (2020)
  16. Gao, Yuan; Shang, Han Lin; Yang, Yanrong: Modelling functional data with high-dimensional error structure (2020)
  17. Gugushvili, Shota; van der Meulen, Frank; Schauer, Moritz; Spreij, Peter: Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient (2020)
  18. Hušková, Marie; Meintanis, Simos G.; Pretorius, Charl: Tests for validity of the semiparametric heteroskedastic transformation model (2020)
  19. Igarashi, Gaku: Nonparametric direct density ratio estimation using beta kernel (2020)
  20. Igarashi, Gaku; Kakizawa, Yoshihide: Multiplicative bias correction for asymmetric kernel density estimators revisited (2020)

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