@inbook {IOPORT.05854226,
    author       = {Hern\'andez, Noslen and Biscay, Rolando J. and Villa-Vialaneix, Nathalie and Talavera, Isneri},
    title        = {A functional density-based nonparametric approach for statistical calibration.},
    year         = {2010},
    booktitle    = {Progress in pattern recognition, image analysis, computer vision, and applications. 15th Iberoamerican congress on pattern recognition, CIARP 2010, Sao Paulo, Brazil, November 8--11, 2010. Proceedings},
    isbn         = {978-3-642-16686-0},
    pages        = {450-457},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-642-16687-7_60},
    abstract     = {Summary: 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.},
    identifier   = {05854226},
}