THE DIMENSION DISTRIBUTION AND QUADRATURE TEST FUNCTIONS
By Art B. Owen. 2003
146 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/67bac978c6116c2c0980c76afab5ac11d3a55d3c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Giles Hooker
- Bogdan E. Popescu
- Ruixue Liu
- Art B. Owen
- Jerome H. Friedman
- Cynthia Rudin
- Jack Hessel
- Christiane Lemieux
Citations in the corpus
- Estimating Mean Dimensionality of Analysis of Variance Decompositions (2006, methodology): “Owen (2003) computed the dimension distribution for some test functions used in QMC.”
- Monte Carlo and Quasi-Monte Carlo Sampling (2009, unknown): “, s [363, 419].”
- Low discrepancy sequences in high dimensions: How well are their projections distributed? (2008, background): “Caflisch et al [3], Owen [18] and Wang and Fang [29] studied the effective dimensions of the problems.”
- Discovering additive structure in black box functions (2004, background): “A standard definition of these is found in the functional ANOVA decomposition, given for example in [11].”
- PREDICTIVE LEARNING VIA RULE ENSEMBLES (2008, methodology): “Roosen (1995), Owen (2001) Jiang and Owen (2001) study interaction effects in “black-box” models using the functional ANOVA decomposition of F ( x ) and product measure.”
- Generalized Functional ANOVA Diagnostics for High-Dimensional Functions of Dependent Variables (2007, methodology): “Modern applications have been in the study of quasi-Monte Carlo methods for integration in Owen (2003).”
- Monte Carlo sampling-based methods for stochastic optimization (2014, background): “Such notion can be made precise, see for instance Owen [178, 179].”
- A Generalized ANOVA Dimensional Decomposition for Dependent Probability Measures (2014, unknown): “Since then, ADD has been studied by numerous researchers in disparate fields of mathematics [24, 8], statistics [16, 6], finance [7], and basic and applied sciences [17], including engineering disciplines, where its polynomial versions have been successfully applied for uncertainty quantification of…”