Name
Abstract | ||
|---|---|---|
We introduce kernel nonparametric tests for Lancaster three-variable interaction and for total independence, using embeddings of signed measures into a reproducing kernel Hilbert space. The resulting test statistics are straightforward to compute, and are used in powerful interaction tests, which are consistent against all alternatives for a large family of reproducing kernels. We show the Lancaster test to be sensitive to cases where two independent causes individually have weak influence on a third dependent variable, but their combined effect has a strong influence. This makes the Lancaster test especially suited to finding structure in directed graphical models, where it outperforms competing nonparametric tests in detecting such V-structures. |
Year | Venue | Field |
|---|---|---|
2013 | NIPS | Kernel (linear algebra),Computer science,Algorithm,Nonparametric statistics,Variables,Artificial intelligence,Graphical model,Statistics,Machine learning,Statistical hypothesis testing,Reproducing kernel Hilbert space |
DocType | Citations | PageRank |
Conference | 5 | 0.46 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dino Sejdinovic | 1 | 443 | 37.96 |
| Arthur Gretton | 2 | 3638 | 226.18 |
| Bergsma, Wicher | 3 | 5 | 0.80 |