Tuning parameter selectors for the smoothly clipped absolute deviation method.

By Hansheng Wang, Runze Li, Chih-Ling Tsai. Biometrika, 2007

804 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d28633983821aa3d886fd6379c52ea78dca51f28

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Hansheng Wang
2. Chenlei Leng
3. Annie Qu
4. Heng Peng
5. Liangjun Su
6. Yuhong Yang
7. Jianqing Fan
8. Jinchi Lv

In Statistics and Probability

1. Chenlei Leng
2. Hansheng Wang
3. Hua Liang
4. Jianqing Fan
5. Yingcun Xia
6. Lan Wang
7. Jinchi Lv
8. Heng Peng

Citations in the corpus

• Estimation and Accuracy after Model Selection (2013, methodology): “For LASSO, we use five-fold cross-validation to select the tuning parameter; while for SCAD we apply BIC (Bayesian information criterion; Wang, Li, and Tsai 2007) for selecting the tuning parameter.”
• Consistent Model Selection for Marginal Generalized Additive Model for Correlated Data (2010, methodology): “A similar strategy can also be found in Huang, Wu and Zhou (2004), Xue and Yang (2006) and Xue (2009).”
• Variable selection for joint mean and dispersion models of the inverse Gaussian distribution (2012, methodology): “Wang et al. (2007) suggested using the BIC for the SCAD estimator in linear models and partially linear models, and proved its model selection consistency property, i.e., the optimal parameter chosen by BIC can identify the true model with probability tending to one.”
• Shrinkage Estimation of the Varying Coefficient Model (2009, methodology): “To achieve the oracle property, the appropriate convergence rates for the shrinkage parameters have been well investigated (Fan and Li 2001; Zou 2006; Zhang and Lu 2007; Wang et al. 2007a).”
• Parametric or nonparametric? A parametricness index for model selection (2011, methodology): “To assess performance of model selection criteria, pointwise asymptotic results (e.g., [17, 27, 39, 43, 46, 48–51, 54, 57, 61, 63, 67, 71, 74, 75]) have been established mostly in terms of either selection consistency or an asymptotic optimality.”
• GENERALIZED ADDITIVE PARTIAL LINEAR MODELS WITH HIGH-DIMENSIONAL COVARIATES (2013, background): “In the literature, both generalized cross-validation (GCV) and Bayesian information criterion (BIC) have been advocated for selection of tuning parameters for penalized sparse models (Huang et al., 2010; Wang, Li, and Tsai, 2007; Wang and Xia, 2009; Wang et al., 2011).”
• Tuning Parameter Selection for the Adaptive Lasso Using ERIC (2015, methodology): “106 For example in linear models with unknown φ ≡ σ2, the variance is estimated afterwards as σ̂(2)λ = 107 (1/n)‖y − xβ̂λ‖(2) (Wang et al., 2007).”
• Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data (2013, methodology): “A similar BIC criterion was suggested by [28], and a generalized information criterion was considered by [13] for tuning parameter selection in penalized likelihood models.”
• Penalized maximum likelihood estimation and variable selection in geostatistics (2011, positive): “a, in the SCAD penalty (2.8) need to be estimated. For computational ease, we fix a = 3.7 as recommended by Fan and Li [8]. To determine λ, we use Bayesian information criterion (BIC); see Wang et al. [26]. In particular, let (2.10) bσ2(λ) = N−1{y −Xβb(λ)}T Γ{θb(λ)}−1{y −Xβb(λ)}, where βb(λ) and θb(λ”
• A Selective Overview of Variable Selection in High Dimensional Feature Space (2009, background): “In Wang, Li and Tsai (2007), it is shown that the model selected by generalized cross-validation using the SCAD penalty contains all important variables, but with nonzero probability includes some unim-portant variables, and that the model selected by using BIC achieves the model selection…”
• Model Selection via Bayesian Information Criterion for Quantile Regression Models (2014, methodology): “The seminal work of Wang and Leng (2007) followed by Wang, Li, and Tsai (2007b) and Wang, Li, and Leng (2009) suggested to use a BIC-type criterion for choosing the shrinkage parameter in penalized least squares and established the model selection consistency of the resulting procedure.”
• Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso (2016, positive): “Let ̃2T̃̃1 ≡ 1−11 (α̃̃1 ; T̃̃1 ) Following Wang et al. (2007), Zhang et al. (2010) and Su and Qian (2014), we propose to select the tuning parameter 1 by minimizing the following information criterion (IC): 1 (1) = ̃ 2 T̃̃1 + 1 (̃1 + 1) (3.3) where 1 is a tuning parameter which plays a similar…”
• On the distribution of the adaptive LASSO estimator (2008, positive): “This is complemented by a result in Zou (2006) who considers the fixed-parameter asymptotic distribution of the LASSO when tuned to act as a consistent model selection procedure. Another contribution is Fan & Li (2001) who derive the fixed-parameter asymptotic distribution of the SCAD estimator when”
• Inference for single-index quantile regression models with profile optimization (2016, methodology): “A similar strategy is used in Wang, Li and Tsai (2007).”
• NONCONCAVE PENALIZED M-ESTIMATION WITH A DIVERGING NUMBER OF PARAMETERS (2011, methodology): “Wang, Li, and Tsai (2007) pointed out that this criterion has a nonignorable overfitting effect even as the sample size goes to infinity.”