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 703 articles , 1 standard article )

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  1. Arif, Osama H.; Eidous, Omar: Fourth-order kernel method for simple linear degradation model (2018)
  2. Bouzebda, Salim; Elhattab, Issam; Seck, Cheikh Tidiane: Uniform in bandwidth consistency of nonparametric regression based on copula representation (2018)
  3. Bouzebda, Salim; Slaoui, Yousri: Nonparametric recursive method for kernel-type function estimators for spatial data (2018)
  4. Castro-Camilo, Daniela; De Carvalho, Miguel; Wadsworth, Jennifer: Time-varying extreme value dependence with application to leading European stock markets (2018)
  5. Chen, Nan; Majda, Andrew J.: Efficient statistically accurate algorithms for the Fokker-Planck equation in large dimensions (2018)
  6. Davies, Tilman M.; Baddeley, Adrian: Fast computation of spatially adaptive kernel estimates (2018)
  7. Davies, Tilman M.; Flynn, Claire R.; Hazelton, Martin L.: On the utility of asymptotic bandwidth selectors for spatially adaptive kernel density estimation (2018)
  8. Kakizawa, Yoshihide: Nonparametric density estimation for nonnegative data, using symmetrical-based inverse and reciprocal inverse Gaussian kernels through dual transformation (2018)
  9. Kim, Andy S. I.; Wand, Matt P.: On expectation propagation for generalised, linear and mixed models (2018)
  10. Lee, Seonmi; Jang, Woncheol; Park, Byeong U.: Kernel excess mass test for multimodality (2018)
  11. Major, John A.: Distortion measures and homogeneous financial derivatives (2018)
  12. Martijn Tennekes: tmap: Thematic Maps in R (2018)
  13. Nakarmi, Janet; Sang, Hailin: Central limit theorem for the variable bandwidth kernel density estimators (2018)
  14. Pardy, Christopher; Galbraith, Sally; Wilson, Susan R.: Integrative exploration of large high-dimensional datasets (2018)
  15. Salhi, Yahia; Thérond, Pierre-E.: Age-specific adjustment of graduated mortality (2018)
  16. Samawi, Hani; Rochani, Haresh; Yin, JingJing; Linder, Daniel; Vogel, Robert: Notes on kernel density based mode estimation using more efficient sampling designs (2018)
  17. Schellhase, Christian; Spanhel, Fabian: Estimating non-simplified vine copulas using penalized splines (2018)
  18. Yang Hu; Carl Scarrott: evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation (2018)
  19. Zhou, Changcong; Tang, Chenghu; Liu, Fuchao; Wang, Wenxuan: A probabilistic representation method for interval uncertainty (2018)
  20. Ziane, Yasmina; Zougab, Nabil; Adjabi, Smail: Birnbaum-Saunders power-exponential kernel density estimation and Bayes local bandwidth selection for nonnegative heavy tailed data (2018)

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