VARIABLE SELECTION IN QUANTILE REGRESSION

By Yichao Wu, Yufeng Liu. 2009

457 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5565df5479f0b470aa19389973e22e86fc8783b1

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1. Jianqing Fan
2. Heng Lian
3. Byeong U. Park
4. Lingzhou Xue
5. Lan Wang
6. Jelena Bradic
7. Hui Zou
8. Mladen Kolar

Citations in the corpus

• Loss Minimization and Parameter Estimation with Heavy Tails (2013, methodology): “For instance, in Zhang et al. (2013), faster parallel kernel ridge regression is achieved by performing loss minimizations on independent samples and then averaging the resulting estimators.”
• Identification of Partially Linear Structure in Additive Models with an Application to Gene Expression Prediction from Sequences (2012, methodology): “After the introduction of lasso in Tibshirani (1996), the penalization approach for variable selection has become increasingly popular (Zou and Hastie, 2005; Zhao and Yu, 2006; Zhang and Huang, 2008; Bickel, Ritov, and Tsybakov, 2009; Wu and Liu, 2009; Yuan, Joseph, and Zou, 2009; Peng et al., 2010)”
• An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression (2015, background): “The penalized quantile regression framework assumes that only a small number of covariates influence the conditional distribution of the response variable given the high-dimensional covariates; however, the sets of relevant covariates may vary when we consider different segments of the conditional…”
• Model Selection via Bayesian Information Criterion for Quantile Regression Models (2014, methodology): “(4.6) We also present the results, when λ is selected by the crossvalidation (five-folds in our simulation) based on the check loss ρτ (·) as was considered in Wu and Liu (2009).”
• Penalized expectile regression: an alternative to penalized quantile regression (2018, methodology): “The main objective of this paper is to develop penalized expectile regression when p is fixed as in Wu and Liu (2009).”
• Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores (2017, background): “ or two-step corrections. In the context of quantile regression, a lot of the early work focused on estimation, prediction and variable selection consistency in low (Li and Zhu,2008;Zou and Yuan,2008;Wu and Liu, 2009) and high-dimensional setting (Belloni and Chernozhukov,2011;Wang et al.,2012;Zheng”
• Inference for single-index quantile regression models with profile optimization (2016, unknown): “Given the initial estimate β̂ 0 τ , the new objective function Qτn(β) is similar to the penalized linear quantile regression problem [Wu and Liu (2009)] with linear predictors G̃(1)τn (XTi β̂ 0 τ , β̂ 0 τ )Xij , 1 ≤ j ≤ p, and adjusted response Y ∗i = Yi − G̃τn ( XTi β̂ 0 τ , β̂ 0 τ ) + G̃(1)τn…”
• STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION (2012, background): “n. Quantile regression (Koenker, 2005) has wide applications in statistics and econometrics. Recently, the spare quantile regression has received much attention (Zou and Yuan, 2008; Li and Zhu, 2008; Wu and Liu, 2009; Belloni and Chernozhukov, 2011; Wang et al., 2012; Fan et al., 2012). We consider ”
• Simultaneous variable selection and estimation in semiparametric modeling of longitudinal/clustered data (2013, unknown): “This dataset contains 2376 observations of CD4 cell counts on 369 men infected with the HIV virus; see Zeger and Diggle (1994) for a detailed description of this dataset.”
• Financial Network Systemic Risk Contributions (2015, unknown): “, Boss, Elsinger, Summer, and Thurner, 2004, or Zhou, 2009, and references therein). Finally, for banking supervisors it is often unclear, how complex structures ultimately translate into dynamic and predictable measures of systemic relevance. The objective of this paper is to develop an easily and ”