论文标题
复杂调查采样中的概率加权簇系数回归模型
Probability Weighted Clustered Coefficients Regression Models in Complex Survey Sampling
论文作者
论文摘要
回归分析通常在调查采样中进行。但是，当关系在不同区域或域之间变化时，现有方法失败。在本文中，我们提出了一个统一的框架，以基于成对惩罚的复杂调查采样下研究小组协变量的效果，并且通过乘数的交替方向方法来解决相关的目标函数。在某些一般条件下研究了所提出方法的理论特性。数值实验证明了所提出的方法在识别群体和线性回归模型和逻辑回归模型的估计效率方面具有优势。
Regression analysis is commonly conducted in survey sampling. However, existing methods fail when the relationships vary across different areas or domains. In this paper, we propose a unified framework to study the group-wise covariate effect under complex survey sampling based on pairwise penalties, and the associated objective function is solved by the alternating direction method of multipliers. Theoretical properties of the proposed method are investigated under some generality conditions. Numerical experiments demonstrate the superiority of the proposed method in terms of identifying groups and estimation efficiency for both linear regression models and logistic regression models.
