A Functional Density-Based Nonparametric Approach for Statistical Calibration

  • Noslen Hernández
  • Rolando J. Biscay
  • Nathalie Villa-Vialaneix
  • Isneri Talavera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)


In this paper a new nonparametric functional method is introduced for predicting a scalar random variable Y from a functional random variable X. The resulting prediction has the form of a weighted average of the training data set, where the weights are determined by the conditional probability density of X given Y, which is assumed to be Gaussian. In this way such a conditional probability density is incorporated as a key information into the estimator. Contrary to some previous approaches, no assumption about the dimensionality of \(\mathbb{E}(X|Y=y)\) is required. The new proposal is computationally simple and easy to implement. Its performance is shown through its application to both simulated and real data.


Root Mean Square Error Mean Square Error Support Vector Regression Conditional Probability Density 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Noslen Hernández
    • 1
  • Rolando J. Biscay
    • 2
    • 3
  • Nathalie Villa-Vialaneix
    • 4
    • 5
  • Isneri Talavera
    • 1
  1. 1.Advanced Technology Application CentreCENATAVCuba
  2. 2.Institute of MathematicsPhysics and CyberneticsCuba
  3. 3.Departamento de Estadística de la Universisad de Valparaíso, CIMFAVChile
  4. 4.Institut de Mathématiques de ToulouseUniversité de ToulouseFrance
  5. 5.Département STIDIUT de PerpignanCarcassonneFrance

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