Sample Average Approximation with Sparsity-Inducing Penalty for High-Dimensional Stochastic Programming. [PDF]
Liu H, Wang X, Yao T, Li R, Ye Y.
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Efficient Post-Shrinkage Estimation Strategies in High-Dimensional Cox's Proportional Hazards Models. [PDF]
Ahmed SE, Arabi Belaghi R, Hussein AA.
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Difference-based ridge-type estimator of parameters in restricted partial linear model with correlated errors. [PDF]
Wu J.
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High-dimensional variable selection accounting for heterogeneity in regression coefficients across multiple data sources. [PDF]
Yu T, Ye S, Wang R.
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Predicting dichotomised outcomes from high-dimensional data in biomedicine. [PDF]
Rauschenberger A, Glaab E.
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Adaptive group bridge estimation for high-dimensional partially linear models. [PDF]
Wang X, Wang M.
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Doubly regularized estimation and selection in linear mixed-effects models for high-dimensional longitudinal data. [PDF]
Li Y +5 more
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I-LAMM FOR SPARSE LEARNING: SIMULTANEOUS CONTROL OF ALGORITHMIC COMPLEXITY AND STATISTICAL ERROR. [PDF]
Fan J, Liu H, Sun Q, Zhang T.
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Folded concave penalized sparse linear regression: sparsity, statistical performance, and algorithmic theory for local solutions. [PDF]
Liu H, Yao T, Li R, Ye Y.
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Variable selection for partially linear proportional hazards model with covariate measurement error. [PDF]
Song X, Wang L, Ma S, Huang H.
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