Approximation capabilities of multilayer feedforward networks
Neural Networks
Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
Bandwidth selection for kernel conditional density estimation
Computational Statistics & Data Analysis
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
KDD'99 competition: knowledge discovery contest
ACM SIGKDD Explorations Newsletter
Adaptive mixtures of local experts
Neural Computation
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In many cases, we observe some variables X that contain predictive information over a scalar variable of interest Y, with (X,Y) pairs observed in a training set. We can take advantage of this information to estimate the conditional density P(Y\X = x). In this paper, we propose a conditional mixture model with hybrid Pareto components to estimate P(Y\X = x).The hybrid Pareto is a Gaussian whose upper tail has been replaced by a generalized Pareto tail. A third parameter, in addition to the location and spread parameters of the Gaussian, controls the heaviness of the upper tail. Using the hybrid Pareto in a mixture model results in a nonparametric estimator that can adapt to multimodality, asymmetry, and heavy tails. A conditional density estimator is built by modeling the parameters of the mixture estimator as functions of X. We use a neural network to implement these functions. Such conditional density estimators have important applications in many domains such as finance and insurance. We show experimentally that this novel approach better models the conditional density in terms of likelihood, compared to competing algorithms: conditional mixture models with other types of components and a classical kernel-based nonparametric model.