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Article Dans Une Revue Revista Investigacion Operacional Année : 2011

A Simulation Study of Functional Density-Based Inverse Regression

Résumé

In this paper a new nonparametric functional method is introduced for predicting a scalar random variable $Y$ on the basis of a functional random variable $X$. The prediction has the form of a weighted average of the training data $y_{i}$, where the weights are determined by the conditional probability density of $X$ given $Y=y_{i}$, 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 $E(X|Y=y)$ or about the distribution of $X$ is required. The new proposal is computationally simple and easy to implement. Its performance is assessed through a simulation study.
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Dates et versions

hal-00654751 , version 1 (22-12-2011)

Identifiants

  • HAL Id : hal-00654751 , version 1

Citer

Noslen Hernandez, Rolando Biscay, Nathalie Villa-Vialaneix, Isneri Talavera. A Simulation Study of Functional Density-Based Inverse Regression. Revista Investigacion Operacional, 2011, 32 (2), pp.146-159. ⟨hal-00654751⟩
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