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0808.0967
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The sparsity and bias of the Lasso selection in high-dimensional linear regression
7 August 2008
Cun-Hui Zhang
Jian Huang
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Papers citing
"The sparsity and bias of the Lasso selection in high-dimensional linear regression"
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Title
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Average case analysis of Lasso under ultra-sparse conditions
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Statistical Learning for Individualized Asset Allocation
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High-dimensional variable selection with heterogeneous signals: A precise asymptotic perspective
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Consistent Feature Selection for Analytic Deep Neural Networks
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Variational approximations of empirical Bayes posteriors in high-dimensional linear models
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Jian Huang
Yuling Jiao
Shuang Zhang
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De-biasing convex regularized estimators and interval estimation in linear models
Pierre C. Bellec
Cun-Hui Zhang
19
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Distributed Machine Learning with Sparse Heterogeneous Data
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Sparse recovery via nonconvex regularized
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Dongya Wu
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Yaohua Hu
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A Survey of Tuning Parameter Selection for High-dimensional Regression
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Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
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Vector-Valued Graph Trend Filtering with Non-Convex Penalties
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Feature Selection for Data Integration with Mixed Multi-view Data
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Optimal Linear Discriminators For The Discrete Choice Model In Growing Dimensions
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Moulinath Banerjee
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High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence
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Arash A. Amini
S. Rangan
A. Fletcher
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