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 840 articles , 1 standard article )

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  1. Hušková, Marie; Meintanis, Simos G.; Pretorius, Charl: Tests for validity of the semiparametric heteroskedastic transformation model (2020)
  2. Igarashi, Gaku; Kakizawa, Yoshihide: Multiplicative bias correction for asymmetric kernel density estimators revisited (2020)
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  4. Makigusa, Natsumi; Naito, Kanta: Asymptotic normality of a consistent estimator of maximum mean discrepancy in Hilbert space (2020)
  5. Qiao, Wanli: Asymptotics and optimal bandwidth for nonparametric estimation of density level sets (2020)
  6. Zhang, Jun; Lin, Bingqing; Feng, Zhenghui: Conditional absolute mean calibration for partial linear multiplicative distortion measurement errors models (2020)
  7. Afere, Benson Ade; Alih, Ekele: On the reduction of global error of multivariate higher-order product polynomial kernels (2019)
  8. Ameijeiras-Alonso, Jose; Crujeiras, Rosa M.; Rodríguez-Casal, Alberto: Mode testing, critical bandwidth and excess mass (2019)
  9. Anevski, Dragi; Fougères, Anne-Laure: Limit properties of the monotone rearrangement for density and regression function estimation (2019)
  10. Anilkumar, P.; Hamsa, K. K.; Ratheesan, K.: Density estimation using polynomial for complete and right censored samples (2019)
  11. Arriaza, A.; Di Crescenzo, A.; Sordo, M. A.; Suárez-Llorens, A.: Shape measures based on the convex transform order (2019)
  12. Aswani, Anil: Statistics with set-valued functions: applications to inverse approximate optimization (2019)
  13. Bahraoui, Zuhair; Bahraoui, M. Amin: Extreme quantiles and tail index of a distribution based on kernel estimator (2019)
  14. Bantis, Leonidas E.; Nakas, Christos T.; Reiser, Benjamin: Construction of confidence intervals for the maximum of the youden index and the corresponding cutoff point of a continuous biomarker (2019)
  15. Benhenni, Karim; Hassan, Ali Hajj; Su, Yingcai: Local polynomial estimation of regression operators from functional data with correlated errors (2019)
  16. Béranger, B.; Duong, T.; Perkins-Kirkpatrick, S. E.; Sisson, S. A.: Tail density estimation for exploratory data analysis using kernel methods (2019)
  17. Bercu, Bernard; Capderou, Sami; Durrieu, Gilles: Nonparametric recursive estimation of the derivative of the regression function with application to sea shores water quality (2019)
  18. Bii, Nelson Kiprono; Onyango, Christopher Ouma; Odhiambo, John: Boundary bias correction using weighting method in presence of nonresponse in two-stage cluster sampling (2019)
  19. Bischofberger, Stephan M.; Hiabu, Munir; Mammen, Enno; Nielsen, Jens Perch: A comparison of in-sample forecasting methods (2019)
  20. Bogomolov, Marina; Davidov, Ori: Order restricted univariate and multivariate inference with adjustment for covariates in partially linear models (2019)

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