epsiwal | R Documentation |
Exact Post Selection Inference with Applications to the Lasso.
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.
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.
This package is maintained as a hobby.
Steven E. Pav shabbychef@gmail.com
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
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