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After variable selection, standard inferential procedures for regression parameters may not be uniformly valid; there is no finite-sample size at which a standard test is guaranteed to approximately attain its nominal size.
Inequalities for the r r th absolute moment of a sum of random variables, 1 ≦ r ≦ 2 1\leqq r\leqq 2
von Bahr, B. and Esseen, C.-G. (1965) · 1965
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Root-N-Consistent Semiparametric Regression
Robinson, P. M. (1988) · 1988
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Estimating exposure effects by modelling the expectation of exposure conditional on confounders
Robins, J. M., Mark, S. D., and Newey, W. K. (1992) · 1992
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Regression Shrinkage and Selection via the Lasso
Tibshirani, R. (1996) · 1996
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Causal Inference from Complex Longitudinal Data
Robins, J. M. (1997) · 1997
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Intensive Insulin Therapy in Critically Ill Patients
Van den Berghe, G., Wouters, P., Weekers, F., Verwaest, C., Bruyninckx, F., Schetz, M., Vlasselaers, D., Ferdinande, P., Lauwers, P., and Bouillon, R. (2001) · 2001
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Model Selection and Inference: Facts and Fiction
Leeb, H. and Pötscher, B. M. (2005) · 2005
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Self-normalized processes: limit theory and statistical applications
De la Peña, V., Lai, T. L., and Shao, Q.-M. (2009) · 2009
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Intensive versus conventional glucose control in critically ill patients
Finfer, S., Chittock, D. R., Su, S. Y.-S., Blair, D., and Foster, D. (2009) · 2009
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Statistics for High-Dimensional Data
Bühlmann, P. and van de Geer, S. (2011) · 2011
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Targeted Learning
van der Laan, M. J. and Rose, S. (2011) · 2011
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Cross-Validated Targeted Minimum-Loss-Based Estimation
Zheng, W. and van der Laan, M. J. (2011) · 2011
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Least squares after model selection in high-dimensional sparse models
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hdm: High-Dimensional Metrics
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Chetverikov, D., Liao, Z., and Chernozhukov, V. (2016) · 2016
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Avagyan, V. and Vansteelandt, S. (2017) · 2017
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A general theory of hypothesis tests and confidence regions for sparse high dimensional models
Ning, Y. and Liu, H. (2017) · 2017
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