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Understanding statistical inference under possibly non-sparse high-dimensional models has gained much interest recently.
Optimal sparsity testing in linear regression model
Carpentier, A. and Verzelen, N. (2019) · 1901
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Asymptotic statistics
Van der Vaart, A. W. (2000) · 2000
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An adaptation theory for nonparametric confidence intervals
Cai, T. T. and Low, M. G. (2004) · 2004
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Adaptive confidence balls
Cai, T. T. and Low, M. G. (2006) · 2006
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Testing against a high dimensional alternative
Goeman, J. J., Van De Geer, S. A., and Van Houwelingen, H. C. (2006) · 2006
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Testing statistical hypotheses
Lehmann, E. L. and Romano, J. P. (2006) · 2006
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Adaptive nonparametric confidence sets
Robins, J. and Van Der Vaart, A. (2006) · 2006
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High-dimensional graphical model selection using l 1 l_{1} -regularized logistic regression
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Adaptive confidence bands
Genovese, C. and Wasserman, L. (2008) · 2008
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Introduction to Nonparametric Estimation
Tsybakov, A. B. (2008) · 2008
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Simultaneous analysis of lasso and dantzig selector
Bickel, P. J., Ritov, Y., and Tsybakov, A. B. (2009) · 2009
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A unified framework for high-dimensional analysis of m m -estimators with decomposable regularizers
Negahban, S., Yu, B., Wainwright, M. J., and Ravikumar, P. K. (2009) · 2009
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Detection boundary in sparse regression
Ingster, Y. I., Tsybakov, A. B., and Verzelen, N. (2010) · 2010
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Introduction to the non-asymptotic analysis of random matrices
Vershynin, R. (2010) · 2010
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Statistics for high-dimensional data: methods, theory and applications
Bühlmann, P. and Van De Geer, S. (2011) · 2011
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On adaptive inference and confidence bands
Hoffmann, M. and Nickl, R. (2011) · 2011
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Minimax rates of estimation for high-dimensional linear regression over l q l_{q} -balls
Raskutti, G., Wainwright, M. J., and Yu, B. (2011) · 2011
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Structure, function and diversity of the healthy human microbiome
Huttenhower, C., Gevers, D., Knight, R., Abubucker, S., Badger, J. H., Chinwalla, A. T., Creasy, H. H., Earl, A. M., FitzGerald, M. G., Fulton, R. S., et al. (2012) · 2012
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False discovery rates in pet and ct studies with texture features: a systematic review
Chalkidou, A., O’Doherty, M. J., and Marsden, P. K. (2015) · 2015
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Revealing rate-limiting steps in complex disease biology: The crucial importance of studying rare, extreme-phenotype families
Chakravarti, A. and Turner, T. N. (2016) · 2016
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Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder
Krishnan, A., Zhang, R., Yao, V., Theesfeld, C. L., Wong, A. K., Tadych, A., Volfovsky, N., Packer, A., Lash, A., and Troyanskaya, O. G. (2016) · 2016
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An expanded view of complex traits: from polygenic to omnigenic
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Confidence intervals for high-dimensional linear regression: Minimax rates and adaptivity
Cai, T. T. and Guo, Z. (2017) · 2017
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Boucheron, S., Lugosi, G., and Massart, P. (2013) · 2013
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Statistical significance in high-dimensional linear models
Bühlmann, P. (2013) · 2013
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Human diseases through the lens of network biology
Furlong, L. I. (2013) · 2013
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Confidence sets in sparse regression
Nickl, R. and van de Geer, S. (2013) · 2013
Cited alongside, same era.
Reconstruction from anisotropic random measurements
Rudelson, M. and Zhou, S. (2013) · 2013
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Confidence intervals and hypothesis testing for high-dimensional regression
Javanmard, A. and Montanari, A. (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., and Dezeure, R. (2014) · 2014
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Eigenprism: inference for high dimensional signal-to-noise ratios
Janson, L., Barber, R. F., and Candes, E. (2017) · 2017
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Goodness-of-fit tests for high dimensional linear models
Shah, R. D. and Bühlmann, P. (2017) · 2017
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Consumer connectivity in a complex, technology-enabled, and mobile-oriented world with smart products
Verhoef, P. C., Stephen, A. T., Kannan, P., Luo, X., Abhishek, V., Andrews, M., Bart, Y., Datta, H., Fong, N., Hoffman, D. L., et al. (2017) · 2017
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Significance testing in non-sparse high-dimensional linear models
Zhu, Y. and Bradic, J. (2017) · 2017
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Accuracy assessment for high-dimensional linear regression
Cai, T. T. and Guo, Z. (2018) · 2018
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Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes
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De-biasing the lasso: Optimal sample size for gaussian designs
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Linear hypothesis testing in dense high-dimensional linear models
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