Dependence Model Assessment and Selection with DecoupleNets
Copyright policy )Dependence Model Assessment and Selection with DecoupleNets
Neural networks are suggested for learning a map from $d$-dimensional samples with any underlying dependence structure to multivariate uniformity in $d'$ dimensions. This map, termed DecoupleNet, is used for dependence model assessment and selection. If the data-generating dependence model was known, and if it was among the few analytically tractable ones, one such transformation for $d'=d$ is Rosenblatt's transform. DecoupleNets have multiple advantages. For example, they only require an available sample and are applicable to $d'
- University of Hong Kong China (People's Republic of)
- University of Waterloo (Canada) Canada
- Chinese Center For Disease Control and Prevention China (People's Republic of)
- University of Waterloo Canada
- University of Waterloo
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Computer Networks and Communications, Machine Learning (stat.ML), Computational Finance (q-fin.CP), Model selection, Statistics - Applications, Statistics - Computation, Machine Learning (cs.LG), Anomaly Detection in High-Dimensional Data, FOS: Economics and business, Quantitative Finance - Computational Finance, Selection (genetic algorithm), Statistics - Machine Learning, Artificial Intelligence, Machine learning, FOS: Mathematics, Applications (stat.AP), Computation (stat.CO), Statistics, 62H99, 65C60, 60E05, 62M45, 00A72, 65C10, 62M10, Computer science, Log Analysis and System Performance Diagnosis, Risk Management (q-fin.RM), Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Mathematics, Quantitative Finance - Risk Management
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Computer Networks and Communications, Machine Learning (stat.ML), Computational Finance (q-fin.CP), Model selection, Statistics - Applications, Statistics - Computation, Machine Learning (cs.LG), Anomaly Detection in High-Dimensional Data, FOS: Economics and business, Quantitative Finance - Computational Finance, Selection (genetic algorithm), Statistics - Machine Learning, Artificial Intelligence, Machine learning, FOS: Mathematics, Applications (stat.AP), Computation (stat.CO), Statistics, 62H99, 65C60, 60E05, 62M45, 00A72, 65C10, 62M10, Computer science, Log Analysis and System Performance Diagnosis, Risk Management (q-fin.RM), Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Mathematics, Quantitative Finance - Risk Management
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