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This paper studies hypothesis testing and parameter estimation in the context of the divide and conquer algorithm.
Theoretical statistics
Cox, D. R · 1974
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Bickel, P. J · 1975
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Regression shrinkage and selection via the lasso
Tibshirani, R · 1996
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Foundations of modern probability
Kallenberg, O · 1997
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Fan, J · 2001
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High-dimensional graphs and variable selection with the lasso
Meinshausen, N · 2006
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The Dantzig selector: statistical estimation when p p is much larger than n n
Candes, E · 2007
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Self-normalized processes
de la Peña, V. H · 2009
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A unified framework for high-dimensional analysis of m m -estimators with decomposable regularizers
Negahban, S · 2009
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Sure independence screening in generalized linear models with NP-dimensionality
Fan, J · 2010
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Introduction to the non-asymptotic analysis of random matrices
Vershynin, R · 2010
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Nearly unbiased variable selection under minimax concave penalty
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Statistics for high-dimensional data: methods, theory and applications
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Chen, X · 2012
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Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
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Challenges of big data analysis
Fan, J · 2014
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Confidence intervals and hypothesis testing for high-dimensional regression
Javanmard, A · 2014
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A scalable bootstrap for massive data
Kleiner, A · 2014
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A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models
Ning, Y · 2014
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On asymptotically optimal confidence regions and tests for high-dimensional models
van de Geer, S · 2014
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A general theory of concave regularization for high-dimensional sparse estimation problems
Zhang, C.-H · 2012
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Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
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Optimal computational and statistical rates of convergence for sparse nonconvex learning problems
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Confidence intervals for low dimensional parameters in high dimensional linear models
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Communication-efficient sparse regression: a one-shot approach
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Regularized M M -estimators with nonconvexity: statistical and algorithmic theory for local optima
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