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Parameter Estimation for a Regression Model

  • Wolfgang Karl Härdle
  • Vladimir Spokoiny
  • Vladimir Panov
  • Weining Wang
Chapter
Part of the Springer Texts in Statistics book series (STS)

Exercise 3.1.

Let a regression function f(⋅) be represented by a linear combination of basis functions\(\Psi _{1}(\cdot ),\ldots,\Psi _{p}(\cdot )\)

Keywords

Kernel Function Gaussian Kernel Linear Regression Line Kernel Density Estimator Gaussian Kernel Function 
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.

References

  1. Fahrmeir, L., & Tutz, G. (1994). Multivariate statistical modelling based on generalized linear models. Heidelberg: Springer.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Wolfgang Karl Härdle
    • 1
  • Vladimir Spokoiny
    • 2
  • Vladimir Panov
    • 3
  • Weining Wang
    • 1
  1. 1.L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. and Econ.Humboldt-Universität zu BerlinBerlinGermany
  2. 2.Weirstrass Institute for Applied Analysis and Stochastics (WIAS)BerlinGermany
  3. 3.Universität Duisburg-EssenEssenGermany

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