Abstract
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.
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Keywords
- Root Mean Square Error
- Mean Square Error
- Support Vector Regression
- Conditional Probability Density
- Functional Data Analysis
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Hernández, N., Biscay, R.J., Villa-Vialaneix, N., Talavera, I. (2010). A Functional Density-Based Nonparametric Approach for Statistical Calibration. In: Bloch, I., Cesar, R.M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2010. Lecture Notes in Computer Science, vol 6419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16687-7_60
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DOI: https://doi.org/10.1007/978-3-642-16687-7_60
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