Breaking the curse of dimensionality in nonparametric testing
By P. Lavergne, Valentin Patilea. 2008
81 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a6fb2079efe0426d0518f98310fb4edd83a634a5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- M. Febrero–Bande
- Wenceslao González-Manteiga
- Eduardo Garc'ia-Portugu'es
- Rosa M. Crujeiras
- Han Lin Shang
In Statistics and Probability
- Wenceslao González-Manteiga
- M. Febrero–Bande
- Eduardo Garc'ia-Portugu'es
- Rosa M. Crujeiras
- Pedro Galeano
- Han Lin Shang
- Dominik Liebl
Citations in the corpus
- An updated review of Goodness-of-Fit tests for regression models (2013, methodology): “For the tests based on smoothing methods, corresponding to process αn, the works by Lavergne and Patilea (2008) and Xia (2009) should be noticed.”
- A Goodness-of-Fit Test for the Functional Linear Model with Scalar Response (2012, methodology): “More recently, Patilea et al. (2012) provide a projection–based test for functional covariate e ffect in a functional regression model with scalar response. In their paper, the authors adapt the tests of Zheng (1996) and Lavergne and Patilea (2008), based on smoothing techniques, to the context of f”