KernSmooth

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: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 937 articles , 1 standard article )

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  1. Basulto-Elias, Guillermo; Carriquiry, Alicia L.; De Brabanter, Kris; Nordman, Daniel J.: Bivariate kernel deconvolution with panel data (2021)
  2. Ben Abdellah, Amal; L’Ecuyer, Pierre; Owen, Art B.; Puchhammer, Florian: Density estimation by randomized quasi-Monte Carlo (2021)
  3. Borisov, Igor S.; Linke, Yuliana Yu.; Ruzankin, Pavel S.: Universal weighted kernel-type estimators for some class of regression models (2021)
  4. Chakraborty, Anirvan; Panaretos, Victor M.: Functional registration and local variations: identifiability, rank, and tuning (2021)
  5. Cui, Qiurong; Xu, Yuqing; Zhang, Zhengjun; Chan, Vincent: Max-linear regression models with regularization (2021)
  6. Deng, Changbao; Jiang, Weinuo; Wang, Shihong: Detecting interactions in discrete-time dynamics by random variable resetting (2021)
  7. Fiebig, Ewelina Marta: On data-driven choice of (\lambda) in nonparametric Gaussian regression via propagation-separation approach (2021)
  8. Ghassabeh, Youness Aliyari; Rudzicz, Frank: Modified subspace constrained mean shift algorithm (2021)
  9. Hu, Shengwei; Wang, Yong: Modal clustering using semiparametric mixtures and mode flattening (2021)
  10. Kirkby, J. Lars; Leitao, Álvaro; Nguyen, Duy: Nonparametric density estimation and bandwidth selection with B-spline bases: a novel Galerkin method (2021)
  11. Kounetas, Konstantinos E.; Polemis, Michael L.; Tzeremes, Nickolaos G.: Measurement of eco-efficiency and convergence: evidence from a non-parametric frontier analysis (2021)
  12. Luati, Alessandra; Novelli, Marco: Explicit-duration hidden Markov models for quantum state estimation (2021)
  13. Markovich, L. A.: Nonparametric estimation of multivariate density and its derivative by dependent data using gamma kernels (2021)
  14. Rattihalli, R. N.; Patil, S. B.: Data dependent asymmetric kernels for estimating the density function (2021)
  15. Rodríguez-Berrio, Felipe; Rodríguez-Cortés, Francisco J.; Mateu, Jorge; Adelfio, Giada: On some statistical properties of the spatio-temporal product density (2021)
  16. Xu, Min; Samworth, Richard J.: High-dimensional nonparametric density estimation via symmetry and shape constraints (2021)
  17. Akyildiz, Ömer Deniz; Crisan, Dan; Míguez, Joaquín: Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization (2020)
  18. Arsalane Chouaib Guidoum: Kernel Estimator and Bandwidth Selection for Density and its Derivatives: The kedd Package (2020) arXiv
  19. Bendich, Paul; Bubenik, Peter; Wagner, Alexander: Stabilizing the unstable output of persistent homology computations (2020)
  20. Beran, Jan; Telkmann, Klaus: On nonparametric ridge estimation for multivariate long-memory processes (2020)

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