Variable selectionThis paper considers a problem of variable selection in quantile regression with autoregressive errors. Recently, Wu and Liu (2009) investigated the oracle properties of the SCAD and adaptive-LASSO penalized quantile regressions under non identical but independent error assumption. We ...
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, ...
Variable selection with group structure: exiting employment at retirement age—a competing risks quantile regression analysisAdaptive group bridgeCompeting risksQuantile regressionStatistical learningWe consider the exit routes of older employees out of employment around retirement age. Our administrative data ...
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....
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
In this paper, we propose a distributed composite quantile regression method for the massive data. The new method can be implemented on the first machine and other machines only need to calculate the gradients of local datasets, which can reduce the communication cost significantly. It also has ...
In this paper, we propose a data-driven penalized weighted composite quantile regression estimation for varying coefficient models with heteroscedasticity, which results in sparse and robust estimators simultaneously. With local weighted composite quantile regression smoothing and adaptive group LASSO, the new...
Partially functional linear quantile regression model and variable selection with censoring indicators MAR Journal of Multivariate Analysis Volume 197,September 2023, Page 105189 Purchase options CorporateFor R&D professionals working in corporate organizations. ...
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 ...
Recently, variable selection based on penalized regression methods has received a great deal of attention, mostly through frequentist's models. This paper investigates regularization regression from Bayesian perspective. Our new method extends the Bayesian Lasso regression (Park and Casella, 2008) through...