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 624 articles , 1 standard article )

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  1. Bott, A.-K.; Felber, T.; Kohler, M.; Kristl, L.: Estimation of a time-dependent density (2017)
  2. Phillips, Peter C.B.; Li, Degui; Gao, Jiti: Estimating smooth structural change in cointegration models (2017)
  3. Armiento, Aurora; Doumic, Marie; Moireau, Philippe; Rezaei, H.: Estimation from moments measurements for amyloid depolymerisation (2016)
  4. Balasubramanian, Krishnakumar; Yu, Kai; Lebanon, Guy: Smooth sparse coding via marginal regression for learning sparse representations (2016)
  5. Belaid, Nawal; Adjabi, Smail; Zougab, Nabil; Kokonendji, Célestin C.: Bayesian bandwidth selection in discrete multivariate associated kernel estimators for probability mass functions (2016)
  6. Bernasconi, Michele; Seri, Raffaello: What are we estimating when we fit Stevens’ power law? (2016)
  7. Delaigle, Aurore; Meister, Alexander; Rombouts, Jeroen: Root-$T$ consistent density estimation in GARCH models (2016)
  8. Dubossarsky, E.; Friedman, J.H.; Ormerod, J.T.; Wand, M.P.: Wavelet-based gradient boosting (2016)
  9. Duong, Tarn: Non-parametric smoothed estimation of multivariate cumulative distribution and survival functions, and receiver operating characteristic curves (2016)
  10. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  11. Grillenzoni, Carlo: Design of blurring mean-shift algorithms for data classification (2016)
  12. Gutmann, Michael U.; Corander, Jukka: Bayesian optimization for likelihood-free inference of simulator-based statistical models (2016)
  13. Kim, Sehee; Li, Yi; Spiegelman, Donna: A semiparametric copula method for Cox models with covariate measurement error (2016)
  14. Kleppe, Tore Selland; Skaug, Hans J.: Bandwidth selection in pre-smoothed particle filters (2016)
  15. Lee, Joonseok; Kim, Seungyeon; Lebanon, Guy; Singer, Yoram; Bengio, Samy: LLORMA: local low-rank matrix approximation (2016)
  16. Li, Linyuan: Nonparametric regression on random fields with random design using wavelet method (2016)
  17. Mohammadi, Faezeh; Izadi, Muhyiddin; Lai, Chin-Diew: On testing whether burn-in is required under the long-run average cost (2016)
  18. Proksch, Katharina: On confidence bands for multivariate nonparametric regression (2016)
  19. Simushkin, D.S.: Empirical estimation of $d$-risks at distinguishing one-sided hypotheses (2016)
  20. Al-Ghufily, N.M.: Testing exponential better than used in convex average class of life distributions based on kernel methods (2015)

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