A consistent modification of a test for independence based on the empirical characteristic function

By Annaliisa Kankainen, N. Ushakov. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Krikamol Muandet
2. Bernhard Schölkopf
3. Kenji Fukumizu
4. David Lopez-Paz
5. Bharath K. Sriperumbudur
6. Arthur Gretton
7. Simos G. Meintanis
8. Marie Husková

Citations in the corpus

• Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “As an aside, Kim and Scott (2012) also exploit this interpretation to robustify the kernel density estimator (KDE) by replacing the squared loss L(k(xi, ·), g) = ‖g − k(xi, ·)‖(2)H in (3.”
• Kernel Bayes' Rule (2010, background): “Thus, it is more akin to the characteristic function approach (see e.g. [20]) to representing probabilities.”
• Multivariate nonparametric test of independence (2017, background): “The paper [19] also discusses testing independence based on empirical characteristic functions, but are only concerned with the joint independence of all the p univariate components.”
• Change Point Analysis based on Empirical Characteristic Functions (2006, methodology): “For recent applications the reader is referred to Epps (1999), Koutrouvelis and Meintanis (1999), Gurtler and Henze (2000) and Kankainen and Ushakov (1998) .”