二元选择分位回归的自适应LASSO改进
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引用本文:李楚进,张翠霞.二元选择分位回归的自适应LASSO改进[J].经济数学,2018,(2):89-97
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作者单位
李楚进,张翠霞 (华中科技大学 数学与统计学院湖北 武汉 430074) 
中文摘要:为避免模型出现过拟合,将自适应LASSO变量选择方法引入二元选择分位回归模型,利用贝叶斯方法构建Gibbs抽样算法并在抽样中设置不影响预测结果的约束条件‖β‖=1以提高抽样值的稳定性.通过数值模拟,表明改进的模型有更为良好的参数估计效率、变量选择功能和分类能力.
中文关键词:应用统计数学;分位回归;自适应LASSO  变量选择;二元选择模型
 
Improvement of Binary Quantile Regression Based on Adaptive LASSO
Abstract:Binary quantile regression model with the adaptive LASSO penalty is proposed for overfitting problems by presenting a Bayesian Gibbs sampling algorithm to estimate parameters. In the process of sampling, the restriction on ‖β‖=1 is motivated to improve the stability of the sampling values. Numerical analysis show there are better improvements of the proposed method in parameter estimation, variable selection and classification.
keywords:applied statistics & mathematics  quantile regression  adaptive LASSO  variable selection  binary regression
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