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 757 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. Aya-Moreno, Carlos; Geenens, Gery; Penev, Spiridon: Shape-preserving wavelet-based multivariate density estimation (2018)
  3. Bouzebda, Salim; Elhattab, Issam; Seck, Cheikh Tidiane: Uniform in bandwidth consistency of nonparametric regression based on copula representation (2018)
  4. Bouzebda, Salim; Slaoui, Yousri: Nonparametric recursive method for kernel-type function estimators for spatial data (2018)
  5. Calonico, Sebastian; Cattaneo, Matias D.; Farrell, Max H.: On the effect of bias estimation on coverage accuracy in nonparametric inference (2018)
  6. Castro-Camilo, Daniela; De Carvalho, Miguel; Wadsworth, Jennifer: Time-varying extreme value dependence with application to leading European stock markets (2018)
  7. Chen, Jia; Li, Degui; Linton, Oliver; Lu, Zudi: Semiparametric ultra-high dimensional model averaging of nonlinear dynamic time series (2018)
  8. Chen, Nan; Majda, Andrew J.: Efficient statistically accurate algorithms for the Fokker-Planck equation in large dimensions (2018)
  9. Clémençon, Stephan; Thomas, Albert: Mass volume curves and anomaly ranking (2018)
  10. Davidov, Ori; Jelsema, Casey M.; Peddada, Shyamal: Testing for inequality constraints in singular models by trimming or winsorizing the variance matrix (2018)
  11. Davies, Tilman M.; Baddeley, Adrian: Fast computation of spatially adaptive kernel estimates (2018)
  12. Davies, Tilman M.; Flynn, Claire R.; Hazelton, Martin L.: On the utility of asymptotic bandwidth selectors for spatially adaptive kernel density estimation (2018)
  13. Guin, Ophélie; Naveau, Philippe; Boreux, Jean-Jacques: Extracting a common signal in tree ring widths with a semi-parametric Bayesian hierarchical model (2018)
  14. Hang, Hanyuan; Steinwart, Ingo; Feng, Yunlong; Suykens, Johan A. K.: Kernel density estimation for dynamical systems (2018)
  15. Holčapek, Michal; Nguyen, Linh; Tichý, Tomáš: Polynomial alias higher degree fuzzy transform of complex-valued functions (2018)
  16. Hsu, Li; Gorfine, Malka; Zucker, David: On estimation of the hazard function from population-based case-control studies (2018)
  17. Huang, Kai; Mi, Jie: A new non-parametric estimator for instant system availability (2018)
  18. Igarashi, Gaku; Kakizawa, Yoshihide: Generalised gamma kernel density estimation for nonnegative data and its bias reduction (2018)
  19. Jan Luts; Shen Wang; John Ormerod; Matt Wand: Semiparametric Regression Analysis via Infer.NET (2018)
  20. Kakizawa, Yoshihide: Nonparametric density estimation for nonnegative data, using symmetrical-based inverse and reciprocal inverse Gaussian kernels through dual transformation (2018)

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