Alhamzawi R, Yu KM (2012) Variable selection in quantile regression via Gibbs sampling. J Appl Stat 39(4): 799-813.Alhamzawi, R. and K. Yu (2011). Variable selection in quantile regression via Gibbs sam- pling. Journal of Applied Statistics. 5...
Quantile regression (QR) has become a popular method of data analysis, especially when the error term is heteroscedastic, due to its relevance in many scientific studies. The ubiquity of high dimensional data has led to a number of variable selection methods for linear/nonlinear QR models and, ...
本文研究了基于面板数据的分位数回归模型的变量选择问题.通过增加改进的自适应Lasso惩罚项,同时实现了固定效应面板数据的分位数回归和变量选择,得到了模型中参数的选择相合性和渐近正态性.随机模拟验证了该方法的有效性.推广了文献[14]的结论.In this paper, we consider
Variable selectionIn this paper, a new composite quantile regression estimation approach is proposed for estimating the parametric part of single-index model. We use local linear CQR for estimating the nonparametric part of SIM when the error distribution is symmetrical. The weighted local linear CQR...
Model selectionOracle propertySCADVarying coefficient modelWeighted composite quantile regressionIn this paper, a new variable selection procedure based on weighted composite quantile regression is proposed for varying coefficient models with a diverging number of parameters. The proposed method is based on ...
We use a flexible dependent competing risks quantile regression model to identify how early and late retirement transitions are related to the information in various registers. Our model selection is guided by machine learning methods, in particular statistical regularization. We use the (adaptive) ...
arXiv:1709.04126v1 [stat.CO] 13 Sep 2017cqrReg: (Composite) Quantile Regression and Variable Selection in RcqrReg: An R Package for Quantile and Composite QuantileRegression and Variable SelectionMatthew Pietrosanu pietrosa@ualberta.caJueyu Gao jueyu@ualberta.caLinglong Kong ∗ lkong@ualberta...
Statistical analysis of massive data is becoming more and more common. Distributed composite quantile regression (CQR) for massive data is proposed in this paper. Specifically, the... 查看全部>>关键词: Massive data Robustness Communication-efficient Composite quantile regression Variable selection 收藏...
Theoretically, we prove that the proposed variable selection method is consistent in variable selection and enjoys oracle property in estimation. Furthermore, the variable selection procedure also works well just as all the data were pooled on a single machine, since it inherits the good properties...
Variable selectionPrimary 62G08Secondary 62J07A new estimation procedure is proposed for the single-index quantile regression model. Compared to existing work, this approach is non-iterative and hence, computationally efficient. The proposed method not only estimates the index parameter and the link ...