Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16687-7_60 | CIARP |
Keywords | Field | DocType |
random variable,conditional probability | Density estimation,Joint probability distribution,Conditional probability distribution,Pattern recognition,Conditional expectation,Posterior probability,Cumulative distribution function,Multivariate random variable,Regular conditional probability,Artificial intelligence,Mathematics | Conference |
Volume | ISSN | ISBN |
6419 | 0302-9743 | 3-642-16686-5 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noslen Hernández | 1 | 7 | 4.57 |
| Rolando J. Biscay | 2 | 12 | 3.54 |
| Nathalie Villa-Vialaneix | 3 | 72 | 10.94 |
| Isneri Talavera | 4 | 17 | 3.75 |