| 董莹,宋立新,石新勇.组合惩罚下联合均值与方差模型的变量选择[J].,2014,54(1):147-151 |
| 组合惩罚下联合均值与方差模型的变量选择 |
| Variable selection via combined penalization in joint mean and variance models |
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| DOI：10.7511/dllgxb201401022 |
| 中文关键词: 组合惩罚 联合均值与方差模型 变量选择 惩罚极大似然估计 |
| 英文关键词: combined penalization joint mean and variance model variable selection penalized maximum likelihood |
| 基金项目:国家自然科学基金资助项目(61175041,11371077)；国家自然科学基金青年基金资助项目(11101062)；中央高校基本科研业务费专项资金资助项目(DUT12LK29)；大连民族学院自主科研基金资助项目(DC120101115). |
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| 中文摘要: |
| 在生产实践和计量经济领域中,控制产品质量的方差就能保证产品的合格品数相对稳定,所以当前学者对联合均值与方差模型的研究倍感兴趣．基于解释变量经常是具有相关关系的实际情况,提出了一种由SCAD惩罚和岭回归混合在一起的组合惩罚,该惩罚充分利用了岭回归能克服解释变量相关性过高对估计效果的影响,同时也证明了这样的惩罚具有相合性和Oracle性质．使用该组合惩罚对联合均值与方差模型进行了变量选择．最后的随机模拟结果表明该模型和方法是有效的． |
| 英文摘要: |
| In the production and econometric area, controlling the variance of the quality of the product can guarantee the stable quality of products. So scholars are very interested in joint mean and variance models nowadays. In general, it is uncommon for explanatory variables to be uncorrelated. A combined penalization, which is mixed by the smoothly clipped absolute deviation (SCAD) penalty and ridge, is proposed. It can outperform the SCAD penalty technique when the correlation among predictors is high. At the same time, the consistency and the Oracle properties of the combined penalization are proved. Then, the combined penalization is used to select variables in joint mean and variance models. The results of stochastic simulation show that this model and method are effective. |
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