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We propose methodology for estimation of sparse precision matrices and statistical inference for their low-dimensional parameters in a high-dimensional setting where the number of parameters $p$ can be much larger than the sample size.
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Chatterjee, A. and Lahiri, S. N. (2013) · 2013
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Javanmard, A. and Montanari, A. (2013) · 2013
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Ng, B., G. Varoquaux, J.-B. P., and Thirion, B. (2013) · 2013
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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. (2013) · 2013
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Belloni, A., Chernozhukov, V., and Hansen, C. (2014) · 2014
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Chatterjee, A. and Lahiri, S. N. (2011) · 2011
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Mazumder, R. and Hastie, T. (2012) · 2012
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Sun, T. and Zhang, C.-H. (2012) · 2012
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Javanmard, A. and Montanari, A. (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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Confidence intervals for high-dimensional inverse covariance estimation
Janková, J. and van de Geer, S. (2015) · 2015
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Asymptotic normality and optimalities in estimation of large gaussian graphical models
Ren, Z., Sun, T., Zhang, C.-H., and Zhou, H. H. (2015) · 2015
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