Discussion: "A significance test for the lasso"

Annals of Statistics

Volumn 42, Issue 2, 2014, Pages 509-517

  Buja, A ^a   Brown, L ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84901723806     PISSN: 00905364     EISSN: None     Source Type: Journal    
DOI: 10.1214/14-AOS1175F     Document Type: Note

References (7)

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2. BUJA, A., BERK, R., BROWN, L., GEORGE, E., PITKIN, E., TRASKIN, M., ZHANG, K. and ZHAO, L. (2014). A conspiracy of random predictors and model violations against classical inference in regression. Available at arXiv:1404.1578.
3. BUJA, A., COOK, D., HOFMANN, H., LAWRENCE, M., LEE, E.-K., SWAYNE, D. F. andWICKHAM, H. (2009). Statistical inference for exploratory data analysis and model diagnostics. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 367 4361-4383. MR2546392
4. GRAZIER G'SELL, M., WAGER, S., CHOULDECHOVA, A. and TIBSHIRANI, R. (2013). False discovery rate control for sequential selection procedures, with application to the lasso. Preprint. Available at arXiv:1309.5352.
5. IOANNIDIS, J. P. A. (2005). Why most published research findings are false. Chance 18 40-47. MR2216666
6. LOCKHART, R., TAYLOR, J., TIBSHIRANI, R. J. and TIBSHIRANI, R. (2014). A significance test for the lasso. Ann. Statist. 42 413-468.
7. WICKHAM, H., COOK, D., HOFMANN, H. and BUJA, A. (2010). Graphical inference for Infovis. IEEE Trans. Vis. Comput. Graph. 16 973-979.