论文标题
无监督的LIU型收缩估计器用于回归模型的混合物
Unsupervised Liu-type Shrinkage Estimators for Mixture of Regression Models
论文作者
论文摘要
在许多应用（例如医学研究）中，感兴趣的人群（例如疾病状况）包括异质亚群。概率回归模型的混合物是将协变量信息纳入种群异质性的最常见技术之一。尽管具有灵活性，但在存在多重共线性问题的情况下，该模型可能会导致不可靠的估计。在本文中，我们通过一种无监督的学习方法来开发LIU型收缩方法，以估计多重共线性中的模型系数。通过分类和EM算法的随机版本评估开发方法的性能。数值研究表明，所提出的方法的表现优于其山脊和最大似然对应物。最后，采用开发的方法来分析50岁及50岁妇女的骨矿物数据。
In many applications (e.g., medical studies), the population of interest (e.g., disease status) comprises heterogeneous subpopulations. The mixture of probabilistic regression models is one of the most common techniques to incorporate the information of covariates into learning of the population heterogeneity. Despite its flexibility, the model may lead to unreliable estimates in the presence of multicollinearity problem. In this paper, we develop Liu-type shrinkage methods through an unsupervised learning approach to estimate the model coefficients in multicollinearity. The performance of the developed methods is evaluated via classification and stochastic versions of EM algorithms. The numerical studies show that the proposed methods outperform their Ridge and maximum likelihood counterparts. Finally, the developed methods are applied to analyze the bone mineral data of women aged 50 and older.
