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Parameter Cascading for High Dimensional Models

  • David Campbell
  • Jiguo Cao
  • Giles Hooker
  • James Ramsay
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

This talk defines a general framework for parameter estimation that synthesizes a variety of common approaches and brings some important new advantages. The parameter cascade involves defining nuisance parameters as functions of structural parameters, and in turn defines structural parameters as functions of complexity parameters.

Keywords

Implicit Function Theorem Complexity Parameter Smoothing Parameter Nuisance Parameter Functional Data Analysis 
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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References

  1. [1]
    Cao, J., Ramsay, J. O.: Parameter cascades and pro ling in functional data analysis. Computational Statistics. 22, 335-351 (2007).CrossRefMathSciNetGoogle Scholar
  2. [2]
    Ramsay, J. O., Hooker, G., Cao, J. and Campbell, D.: Parameter estimation for di erential equations: A generalized smoothing approach (with discussion). Journal of the Royal Statistical Society. Series B. 69, 741-796 (2007).CrossRefMathSciNetGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg 2008

Authors and Affiliations

  • David Campbell
    • 1
  • Jiguo Cao
    • 1
  • Giles Hooker
    • 1
  • James Ramsay
    • 1
  1. 1.Dept. of PsychologyMontrealCanada

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