Statistical analysis with missing data
Statistical analysis with missing data
Probability and statistics
Learning from Examples with Information Theoretic Criteria
Journal of VLSI Signal Processing Systems
Stochastic Modeling in Broadband Communications Systems
Stochastic Modeling in Broadband Communications Systems
Stochastic Modeling: From Pattern Classification to Speech Recognition and Translation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Learning from Incomplete Data
Learning Chance Probability Functions for Shape Retrieval or Classification
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
Neural Systems with Numerically-Matched Input---Output Statistic: Variate Generation
Neural Processing Letters
Nonparametric hypothesis tests for statistical dependency
IEEE Transactions on Signal Processing
Speaker association with signal-level audiovisual fusion
IEEE Transactions on Multimedia
Generalized information potential criterion for adaptive system training
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Advanced search algorithms for information-theoretic learning with kernel-based estimators
IEEE Transactions on Neural Networks
Asymmetric variate generation via a parameterless dual neural learning algorithm
Computational Intelligence and Neuroscience - Processing of Brain Signals by Using Hemodynamic and Neuroelectromagnetic Modalities
Hi-index | 0.00 |
Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not match (i.e., there are "holes" in the data) or when the data sets have been acquired independently. Also, statistical modeling is useful when the amount of available data is enough to show relevant statistical features of the phenomenon underlying the data. We propose to tackle the problem of statistical modeling via a neural (nonlinear) system that is able to match its input-output statistic to the statistic of the available data sets. A key point of the new implementation proposed here is that it is based on look-up-table (LUT) neural systems, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure.