nonpar: Nonparametric two-sample testing using kernel-based test...
In youngser/gmmase: Graph Spectral Clustering

Description Usage Arguments Value Author(s)

This is a simple implementation of the kernel-based test statistic for the nonparametric two-sample testing problem of given X_1, X_2, …, X_n i.i.d. F and Y_1, Y_2, …, Y_m i.i.d. G, test the null hypothesis of F = G against the alternative hypothesis of F \not = G. The test statistic is based on embedding F and G into a reproducing kernel Hilbert space and then compute a distance between the resulting embeddings. For this primitive, the Hilbert space is associated with the Gaussian kernel.

1	nonpar(Xhat1, Xhat2, sigma = 0.5)

`Xhat1`	a n x d matrix
`Xhat2`	a n x d matrix
`sigma`	a bandwidth for the Gaussian kernel

T A scalar value T such that T is near 0 if the rows of X and Y are from the same distribution and T far from 0 if the rows of X and Y are from different distribution.

Youngser Park <youngser@jhu.edu>

youngser/gmmase documentation built on May 30, 2019, 7:19 p.m.