Two-Sample Testing in High-Dimensional Models (Differential Network & Differential Regression)


DiffNet: Network inference is subject to statistical uncertainty and observed differences between two networks inferred from two datasets may be due to noise and variability in estimation rather than any true difference in underlying network topology. Significance testing for network differences is a challenging statistical problem, involving high-dimensional estimation and comparison of non-nested hypotheses. Our recently developed method "differential network" performs formal two-sample testing between high-dimensional Gaussian graphical models (GGMs) and is implemented in the R-package DiffNet. For technical details of the approach we refer the reader to: Städler, N. and Mukherjee, S. (2013). Two-Sample Testing in High-Dimensional Models. Preprint arXiv:1210.4584. Manual.


DiffRegr: The R-package DiffRegr performs formal two-sample testing between high-dimensional regression models. For technical details of the approach we refer the reader to: Städler, N. and Mukherjee, S. (2013). Two-Sample Testing in High-Dimensional Models. Preprint arXiv:1210.4584. Manual.




Home