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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.
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
中图分类号 O212.1 文献标识码 A
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.
Key words applied statistics
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
中图分类号 O212.1 文献标识码 A
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.
Key words applied statistics