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 878 articles , 1 standard article )

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  1. Casa, Alessandro; Chacón, José E.; Menardi, Giovanna: Modal clustering asymptotics with applications to bandwidth selection (2020)
  2. Crespi, Giovanni Paolo; Mastrogiacomo, Elisa: Qualitative robustness of set-valued value-at-risk (2020)
  3. Cui, Zhenyu; Kirkby, Justin Lars; Nguyen, Duy: Nonparametric density estimation by B-spline duality (2020)
  4. Hušková, Marie; Meintanis, Simos G.; Pretorius, Charl: Tests for validity of the semiparametric heteroskedastic transformation model (2020)
  5. Igarashi, Gaku: Nonparametric direct density ratio estimation using beta kernel (2020)
  6. Igarashi, Gaku; Kakizawa, Yoshihide: Multiplicative bias correction for asymmetric kernel density estimators revisited (2020)
  7. Janssen, Paul; Swanepoel, Jan; Veraverbeke, Noël: A note on the behaviour of a kernel-smoothed kernel density estimator (2020)
  8. Kolomvatsos, Kostas; Anagnostopoulos, Christos: A probabilistic model for assigning queries at the edge (2020)
  9. Makigusa, Natsumi; Naito, Kanta: Asymptotic normality of a consistent estimator of maximum mean discrepancy in Hilbert space (2020)
  10. Mcswiggan, Greg; Baddeley, Adrian; Nair, Gopalan: Estimation of relative risk for events on a linear network (2020)
  11. Peng, Jiayu; Lin, Dennis K. J.: Small screening design when the overall variance is unknown (2020)
  12. Perrin, Guillaume; Soize, Christian: Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework (2020)
  13. Qiao, Wanli: Asymptotics and optimal bandwidth for nonparametric estimation of density level sets (2020)
  14. Sun, Yiguo: The LLN and CLT for U-statistics under cross-sectional dependence (2020)
  15. Tong, Hongzhi; Wu, Qiang: Moving quantile regression (2020)
  16. Tsuruta, Yasuhito; Sagae, Masahiko: Theoretical properties of bandwidth selectors for kernel density estimation on the circle (2020)
  17. Zhang, Chenguang; He, Hua; Li, Jian; Tang, Wan: Doubly robust kernel density estimation when group membership is missing at random (2020)
  18. Zhang, Jun; Lin, Bingqing; Feng, Zhenghui: Conditional absolute mean calibration for partial linear multiplicative distortion measurement errors models (2020)
  19. Afere, Benson Ade; Alih, Ekele: On the reduction of global error of multivariate higher-order product polynomial kernels (2019)
  20. Ameijeiras-Alonso, Jose; Crujeiras, Rosa M.; Rodríguez-Casal, Alberto: Mode testing, critical bandwidth and excess mass (2019)

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