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We present a new method for post-selection inference for L1 (lasso)-penalized likelihood models, including generalized regression models.
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Lee, J., Sun, D., Sun, Y. and Taylor, J. (2013), Exact post-selection inference, with application to the Lasso · 2013
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Reid, S., Tibshirani, R. and Friedman, J. (2013), ‘A Study of Error Variance Estimation in Lasso Regression’, ArXiv e-prints; to appear Statistica Sinica
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arXiv: 1401.3889; submitted
Taylor, J., Lockhart, R., Tibshirani 2 , R. and Tibshirani, R. (2014), Post-selection adaptive inference for least angle regression and the Lasso · 2014
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Tian, X. and Taylor, J. E. (2014), ‘Asymptotics of selective inference’, ArXiv e-prints
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Zhang, C.-H. and Zhang, S. (2014), ‘Confidence intervals for low-dimensional parameters with high-dimensional data’, Journal of the Royal Statistical Society Series B
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Loftus, J. R. (2015), ‘Selective inference after cross-validation’, ArXiv e-prints
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Taylor, J. and Tibshirani, R. J. (2015), ‘Statistical learning and selective inference’, Proceedings of the National Academy of Sciences
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Fithian, W., Sun, D. and Taylor, J. (2014), ‘Optimal inference after model selection’, ArXiv e-prints
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Javanmard, A. and Montanari, A. (2014), ‘Confidence intervals and hypothesis testing for high-dimensional regression’, Journal of Machine Learning Research
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Tian, X. and Taylor, J. E. (2015), ‘Selective inference with a randomized response’, ArXiv e-prints
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Tibshirani, R. J., Rinaldo, A., Tibshirani, R. and Wasserman, L. (2015), ‘Uniform Asymptotic Inference and the Bootstrap After Model Selection’, ArXiv e-prints
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Buja, A., Berk, R., Brown, L., George, E., Pitkin, E., Traskin, M., Zhang, K. and Zhao, L. (2016), A Conspiracy of Random X and Nonlinearity against Classical Inference in Linear Regression · 2016
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