Smoothing techniques for curve estimation : proceedings of a workshop held in Heidelberg, April 2-4, 1979

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Bibliografiske detaljer
Institution som forfatter: Workshop Smoothing techniques for curve estimation :Heidelberg, Allemagne
Andre forfattere: Rosenblatt, Murray, 1926-2019, mathématicien (Directeur de la publication), Gasser, Theodor A., 1941- (Directeur de la publication)
Format: Livre numérique
Sprog:Anglais
Udgivet: Berlin [etc.] : Springer [201. ?].
Serier:Lecture notes in mathematics 757
Fag:
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Kommentar: Preface : "The workshop ... has taken place as part of the activities of the Sonderforschungsbereich 123, "Stochastic Mathematical Models."
Numérisation de l'édition de Berlin : Springer, 1979
Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Smoothing techniques for curve estimation, proceedings of a workshop held in Heidelberg, April 2-4, 1979, edited by Th. Gasser and M. Rosenblatt, 1979, Berlin, Springer-Verlag, 1 vol. (245 p.), Lecture notes in mathematics, 3-540-09706-6
• Smoothing Techniques for Curve Estimation, Texte imprimé, 9783662181867
Indholdsfortegnelse:
  • Nonparametric curve estimation
  • A tree-structured approach to nonparametric multiple regression
  • Kernel estimation of regression functions
  • Total least squares
  • Some theoretical results on Tukey s 3R smoother
  • Bias- and efficiency-robustness of general M-estimators for regression with random carriers
  • Approximate conditional-mean type smoothers and interpolators
  • Optimal convergence properties of kernel estimates of derivatives of a density function
  • Density quantile estimation approach to statistical data modelling
  • Global measures of deviation for kernel and nearest neighbor density estimates
  • Some comments on the asymptotic behavior of robust smoothers
  • Cross-validation techniques for smoothing spline functions in one or two dimensions
  • Convergence rates of "thin plate" smoothing splines wihen the data are noisy.