Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Smoothing splines estimators in functional linear regression with errors-in-variables
Computational Statistics & Data Analysis
Support vector regression methods for functional data
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A review of Bayesian neural networks with an application to near infrared spectroscopy
IEEE Transactions on Neural Networks
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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 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.