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 786 articles , 1 standard article )

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  1. Anevski, Dragi; Fougères, Anne-Laure: Limit properties of the monotone rearrangement for density and regression function estimation (2019)
  2. Arriaza, A.; Di Crescenzo, A.; Sordo, M. A.; Suárez-Llorens, A.: Shape measures based on the convex transform order (2019)
  3. Bahraoui, Zuhair; Bahraoui, M. Amin: Extreme quantiles and tail index of a distribution based on kernel estimator (2019)
  4. 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)
  5. Benhenni, Karim; Hassan, Ali Hajj; Su, Yingcai: Local polynomial estimation of regression operators from functional data with correlated errors (2019)
  6. Béranger, B.; Duong, T.; Perkins-Kirkpatrick, S. E.; Sisson, S. A.: Tail density estimation for exploratory data analysis using kernel methods (2019)
  7. Bogomolov, Marina; Davidov, Ori: Order restricted univariate and multivariate inference with adjustment for covariates in partially linear models (2019)
  8. Bongiorno, Enea G.; Goia, Aldo: Describing the concentration of income populations by functional principal component analysis on Lorenz curves (2019)
  9. Hess, Christian; Seri, Raffaello: Generic consistency for approximate stochastic programming and statistical problems (2019)
  10. Margaret Roberts; Brandon Stewart; Dustin Tingley: stm: An R Package for Structural Topic Models (2019) not zbMATH
  11. Morris, Katherine; Punzo, Antonio; McNicholas, Paul D.; Browne, Ryan P.: Asymmetric clusters and outliers: mixtures of multivariate contaminated shifted asymmetric Laplace distributions (2019)
  12. Rossini, Jacopo; Canale, Antonio: Quantifying prediction uncertainty for functional-and-scalar to functional autoregressive models under shape constraints (2019)
  13. Zhang, Jin-Ting; Cheng, Ming-Yen; Wu, Hau-Tieng; Zhou, Bu: A new test for functional one-way ANOVA with applications to ischemic heart screening (2019)
  14. Arif, Osama H.; Eidous, Omar: Fourth-order kernel method for simple linear degradation model (2018)
  15. Aya-Moreno, Carlos; Geenens, Gery; Penev, Spiridon: Shape-preserving wavelet-based multivariate density estimation (2018)
  16. Bouzebda, Salim; Elhattab, Issam; Seck, Cheikh Tidiane: Uniform in bandwidth consistency of nonparametric regression based on copula representation (2018)
  17. Bouzebda, Salim; Slaoui, Yousri: Nonparametric recursive method for kernel-type function estimators for spatial data (2018)
  18. Calonico, Sebastian; Cattaneo, Matias D.; Farrell, Max H.: On the effect of bias estimation on coverage accuracy in nonparametric inference (2018)
  19. Castro-Camilo, Daniela; De Carvalho, Miguel; Wadsworth, Jennifer: Time-varying extreme value dependence with application to leading European stock markets (2018)
  20. Chen, Jia; Li, Degui; Linton, Oliver; Lu, Zudi: Semiparametric ultra-high dimensional model averaging of nonlinear dynamic time series (2018)

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