epsiwal: Exact Post Selection Inference with Applications to the...

In epsiwal: Exact Post Selection Inference with Applications to the Lasso

Description

Exact Post Selection Inference with Applications to the Lasso.

Details

This simple package supports the simple procedure outlined in Lee et al. where one observes a normal random variable, then performs inference conditional on some linear inequalities.

Suppose y is multivariate normal with mean μ and covariance Σ. Conditional on Ay <= b, one can perform inference on eta'mu by transforming y to a truncated normal. Similarly one can invert this procedure and find confidence intervals on eta'mu.

Legal Mumbo Jumbo

epsiwal is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

Note

This package is maintained as a hobby.

Author(s)

Steven E. Pav [e-mail]

References

Lee, J. D., Sun, D. L., Sun, Y. and Taylor, J. E. "Exact post-selection inference, with application to the Lasso." Ann. Statist. 44, no. 3 (2016): 907-927. doi:10.1214/15-AOS1371. https://arxiv.org/abs/1311.6238

Pav, S. E. "Conditional inference on the asset with maximum Sharpe ratio." Arxiv e-print (2019). http://arxiv.org/abs/1906.00573

epsiwal documentation built on July 2, 2019, 5:07 p.m.