@inproceedings{5375,
  title = {Nonparametric Independence Tests: Space
  Partitioning and Kernel Approaches},
  journal = {Algorithmic Learning Theory: 19th International Conference (ALT08)},
  booktitle = {ALT08},
  abstract = {Three simple and explicit procedures for testing the independence
  of two multi-dimensional random variables are described. Two
  of the associated test statistics (L1, log-likelihood) are defined when the
  empirical distribution of the variables is restricted to finite partitions.
  A third test statistic is defined as a kernel-based independence measure.
  All tests reject the null hypothesis of independence if the test statistics
  become large. The large deviation and limit distribution properties of all
  three test statistics are given. Following from these results, distributionfree
  strong consistent tests of independence are derived, as are asymptotically
  alpha-level tests. The performance of the tests is evaluated experimentally
  on benchmark data.},
  pages = {183-198},
  editors = {Freund, Y. , L. Gy{\"o}rfi, G. Turán, T. Zeugmann},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = oct,
  year = {2008},
  author = {Gretton, A. and Gy{\"o}rfi, L.},
  doi = {10.1007/978-3-540-87987-9_18},
  month_numeric = {10}
}