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We study tools for inference conditioned on model selection events that are defined by the generalized lasso regularization path.
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Hastie, T., Tibshirani, R. & Friedman, J. (2009), The Elements of Statistical Learning; Data Mining, Inference and Prediction · 2009
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Tibshirani, R. J. & Taylor, J. (2011), ‘The solution path of the generalized lasso’, Annals of Statistics
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Lee, J. & Taylor, J. (2014), ‘Exact post model selection inference for marginal screening’, Advances in Neural Information Processing Systems
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Lockhart, R., Taylor, J., Tibshirani, R. J. & Tibshirani, R. (2014), ‘A significance test for the lasso’, Annals of Statistics
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arXiv: 1405.3920
Loftus, J. & Taylor, J. (2014), A significance test for forward stepwise model selection · 2014
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arXiv: 1405.3340
Reid, S., Taylor, J. & Tibshirani, R. (2014), Post-selection point and interval estimation of signal sizes in Gaussian samples · 2014
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Tibshirani, R. J. (2014), ‘Adaptive piecewise polynomial estimation via trend filtering’, Annals of Statistics
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arXiv: 1512.02565
Fithian, W., Taylor, J., Tibshirani, R. & Tibshirani, R. J. (2015), Selective sequential model selection · 2015
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2012
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Tibshirani, R. J. & Taylor, J. (2012), ‘Degrees of freedom in lasso problems’, Annals of Statistics
2012
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Berk, R., Brown, L., Buja, A., Zhang, K. & Zhao, L. (2013), ‘Valid post-selection inference’, Annals of Statistics
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Jandhyala, V., Fotopoulos, S., Macneill, I. & Liu, P. (2013), ‘Inference for single and multiple change-points in time series’, Journal of Time Series Analysis
2013
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arXiv: 1410.8260
Choi, Y., Taylor, J. & Tibshirani, R. (2014), Selecting the number of principal components: estimation of the true rank of a noisy matrix · 2014
Cited alongside, same era.
arXv: 1410.2597
Fithian, W., Sun, D. & Taylor, J. (2014), Optimal inference after model selection · 2014
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Frick, K., Munk, A. & Sieling, H. (2014), ‘Multiscale change point inference’, Journal of the Royal Statistical Society. Series B: Statistical Methodology
2014
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arXiv: 1501.03588
Tian, X. & Taylor, J. (2015 a · 2015
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arXiv: 1507.06739
Tian, X. & Taylor, J. (2015 b · 2015
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arXiv: 1506.06266
Tibshirani, R. J., Rinaldo, A., Tibshirani, R. & Wasserman, L. (2015), Uniform asymptotic inference and the bootstrap after model selection · 2015
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Arnold, T. & Tibshirani, R. J. (2016), ‘Efficient implementations of the generalized lasso dual path algorithm’, Journal of Computational and Graphical Statistics
2016
Closest in time.
Grazier G’Sell, M., Wager, S., Chouldechova, A. & Tibshirani, R. (2016), ‘Sequential selection procedures and false discovery rate control’, Journal of the Royal Statistical Society: Series B
2016
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To appear
Lee, J., Sun, D., Sun, Y. & Taylor, J. (2016), ‘Exact post-selection inference with application to the lasso’, Annals of Statistics · 2016
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To appear
Tibshirani, R. J., Taylor, J., Lockhart, R., & Tibshirani, R. (2016), ‘Exact post-selection inference for sequential regression procedures’, Journal of the American Statistical Association · 2016
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To appear in Journal of Machine Learning Research
Wang, Y.-X., Sharpnack, J., Smola, A. & Tibshirani, R. J. (2016), ‘Trend filtering on graphs’ · 2016
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