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We propose a methodology for testing linear hypothesis in high-dimensional linear models.
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Square-root lasso: pivotal recovery of sparse signals via conic programming
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Minimax rates of estimation for high-dimensional linear regression over-balls
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Predictive regressions with time-varying coefficients
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Ning, Y. and Liu, H. (2014) · 2014
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On asymptotically optimal confidence regions and tests for high-dimensional models
Van de Geer, S., Bühlmann, P., Ritov, Y., Dezeure, R., et al. (2014) · 2014
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Confidence intervals for low dimensional parameters in high dimensional linear models
Zhang, C.-H. and Zhang, S. S. (2014) · 2014
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Uniform post-selection inference for least absolute deviation regression and other z-estimation problems
Belloni, A., Chernozhukov, V., and Kato, K. (2015) · 2015
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Confidence sets in sparse regression
Nickl, R., van de Geer, S., et al. (2013) · 2013
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Reconstruction from anisotropic random measurements
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Valid post-selection and post-regularization inference: An elementary, general approach
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De-biasing the Lasso: Optimal Sample Size for Gaussian Designs
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Fred-md: a monthly database for macroeconomic research
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LASSO Methods for Gaussian Instrumental Variables Models
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Accuracy Assessment for High-dimensional Linear Regression
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Semi-parametric efficiency bounds and efficient estimation for high-dimensional models
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Decorrelated feature space partitioning for distributed sparse regression
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