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 1018 articles , 1 standard article )

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  1. Adam, C.; Gijbels, I.: Local polynomial expectile regression (2022)
  2. Ali, Taha Hussein: Modification of the adaptive Nadaraya-Watson kernel method for nonparametric regression (simulation study) (2022)
  3. Baíllo, A.; Chacón, J. E.: A new selection criterion for statistical home range estimation (2022)
  4. Bentoumi, Rachid; Alvo, Mayer; Mesfioui, Mhamed: Dependence measure for length-biased survival data using kernel density estimation with a regression procedure (2022)
  5. Bouzebda, Salim; Didi, Sultana: Some results about kernel estimators for function derivatives based on stationary and ergodic continuous time processes with applications (2022)
  6. Bouzebda, Salim; Slaoui, Yousri: Nonparametric recursive method for moment generating function kernel-type estimators (2022)
  7. Coblenz, Maximilian; Grothe, Oliver; Herrmann, Klaus; Hofert, Marius: Smooth bootstrapping of copula functionals (2022)
  8. Coretto, Pietro: Estimation and computations for Gaussian mixtures with uniform noise under separation constraints (2022)
  9. Gao, Jia-Xing; Jiang, Da-Quan; Qian, Min-Ping: Adaptive manifold density estimation (2022)
  10. Guan, Tianyu; Nguyen, Robert; Cao, Jiguo; Swartz, Tim: In-game win probabilities for the National Rugby League (2022)
  11. Guo, Linruo; Song, Weixing; Shi, Jianhong: Estimating multivariate density and its derivatives for mixed measurement error data (2022)
  12. Hörmann, Siegfried; Jammoul, Fatima: Consistently recovering the signal from noisy functional data (2022)
  13. Kakizawa, Yoshihide: Multivariate elliptical-based Birnbaum-Saunders kernel density estimation for nonnegative data (2022)
  14. Kulasekera, K. B.; Siriwardhana, Chathura: Multi-response based personalized treatment selection with data from crossover designs for multiple treatments (2022)
  15. L’Ecuyer, Pierre; Puchhammer, Florian; Abdellah, Amal Ben: Monte Carlo and quasi-Monte Carlo density estimation via conditioning (2022)
  16. Manderson, Andrew A.; Goudie, Robert J. B.: A numerically stable algorithm for integrating Bayesian models using Markov melding (2022)
  17. Matias D. Cattaneo, Michael Jansson, Xinwei Ma: lpdensity: Local Polynomial Density Estimation and Inference (2022) not zbMATH
  18. Musleh, Rola; Helu, Amal; Samawi, Hani: Kernel-based estimation of (P(X < Y)) when (X) and (Y) are dependent random variables based on progressive type II censoring (2022)
  19. Qarmalah, Najla M.: Localized mixture models for prediction with application (2022)
  20. Schuster, Michael; Strauch, Elisa; Gugat, Martin; Lang, Jens: Probabilistic constrained optimization on flow networks (2022)

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