Shrinkage tuning parameter selection with a diverging number of parameters

By Hansheng Wang, Bo Li, Chenlei Leng. 2009

459 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4ca5a833b8464a1e7ab46d04db6aeb2794eca848

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In Statistics and Probability

Citations in the corpus

Variable selection using pseudo-variables (2018, background): “Accordingly, there is an extensive literature on tuning parameter selection including information-based criteria (Chen and Chen, 2008; Wang et al., 2009; Zhang et al., 2010; Fan and Tang, 2013; Hui et al., 2015), resampling methods (Hall et al.”
Gradient-based kernel method for feature extraction and variable selection (2012, methodology): “To choose the parameter θ, a BIC-based method is often used in sparse variable selection [27, 31] with theoretical guarantee of model consistency.”
A Model-Averaging Approach for High-Dimensional Regression (2014, methodology): “We also conduct a simulation study to illustrate the merits of the proposed approach over several existing methods, including lasso, group lasso, forward regression, Phase Coupled (PC)-simple algorithm, Akaike information criterion (AIC) model-averaging, Bayesian information criterion (BIC) model-av”
Tuning Parameter Selection for the Adaptive Lasso Using ERIC (2015, methodology): “Thanks to Berwin Turlach for useful discussions and Eric Foster for inspiration. settings (e.g., Wang, Li, and Leng 2009; Wang and Zhu 2011; Fan and Tang 2012).”
Model Selection via Bayesian Information Criterion for Quantile Regression Models (2014, methodology): “Recently, Wang, Li, and Leng (2009) and Chen and Chen (2008) found that the ordinary BIC is too liberal for large model spaces and proposed its modifications that work when the number of variables increases with the sample size.”
Tuning parameter selection in high dimensional penalized likelihood (2013, methodology): “We find that, when p grows polynomially with sample size n, the modified BIC (Wang et al., 2009) can still be successful in tuning parameter selection.”
Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model (2019, background): “The BIC criterion Equation (4.17) is in fact inspired by Wang, Li, and Leng (2009).4 Although the value of a is unknown in the definition of B, the optimal values λT and λ′T are in fact independent of a because of the logarithmic operation in the first term of Equation (4.17).”
Multiple predicting K-fold cross-validation for model selection (2018, methodology): “The method by Wang, Li, and Leng (2009) allows p to increase, but requires n > p for variable-selection consistency.”
An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression (2015, unknown): “…j+1 ← β (k)(r)j for j = p then r ← r + 1 j ← j + 1 (mod p) until β(k)(r)j converge to β ∗ β (k) ← β∗ until β (k) converge to β̂ then return (β̂) High-dimensional BIC-type criterion for nonconvex penalized least-squares regression with diverging p was recently investigated by Wang, Li, and Leng…”
Adaptive penalized quantile regression for high dimensional data (2013, methodology): “The first limitation is that one of the requirements in Wang et al. (2009) is pon, which may not be satisfied in the ultra-high dimensional problem.”
Identifying a Minimal Class of Models for High-dimensional Data (2015, background): “They also show that the choice of exponential weights corresponds to minimizing a speciﬁc, yet relevant, penalized problem.”