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Kernel and Local Weighted Least Squares Methods

  • Hans-Georg Müller
Part of the Lecture Notes in Statistics book series (LNS, volume 46)

Abstract

It is assumed from now on that in the model (2.1)
$$ {\text{Y}}_i {\text{,n-g(t}}_{i,n} ) + \varepsilon _{i,n} {\text{ ,i = 1,}}...{\text{,n}} $$
where usually indices n are omitted, 0≤t1≤t2≤…≤tn≤l without loss of generality. As in (2.1), the errors are assumed to be i.i.d. with Eεi=0, Eεi2= σ2∞ (for most considerations in Chapters 4–6, uncorrelatedness is sufficient).

Keywords

Mean Square Error Kernel Estimate Kernel Estimator Smoothing Spline Boundary Modification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Hans-Georg Müller
    • 1
    • 2
  1. 1.Institute of Medical StatisticsUniversity of Erlangen-NürnbergErlangenFederal Republic of Germany
  2. 2.Division of StatisticsUniversity of CaliforniaDavisUSA

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