Smooth expectiles for panel data using penalized splines
Smooth expectiles for panel data using penalized splines
复制标题
DOI：
10.1007/s11222-015-9621-2
复制
发表时间：
2017
影响因子：
2.2
通讯作者：
Linda Schulze Waltrup;G. Kauermann
中科院分区：
文献类型：
--
作者：
Linda Schulze Waltrup;G. Kauermann
期望回归是近年来流行的一个话题。它包括普通均值回归作为特殊情况，但更一般，因为它也提供了对分布的非中心部分建模的可能性。半参数期望模型是近年来发展起来的一种新的期望模型，它可以很容易地用现代软件如R。我们通过允许面板观察来扩展模型类，即在同一个体上进行重复测量的聚类数据。随机（个体）效应被纳入模型中，该模型解释了数据中的依赖结构。我们拟合可预期的表，这意味着不是估计单个可预期的，而是同时估计整个范围的可预期的。所提出的模型允许多个协变量，其中采用惩罚样条的半参数方法来拟合光滑的预期曲线。我们将我们的方法应用于德国社会经济小组的面板数据。
Expectile regression is a topic which became popular in the last years. It includes ordinary mean regression as special case but is more general as it offers the possibility to also model non-central parts of a distribution. Semi-parametric expectile models have recently been developed and it is easy to perform flexible expectile estimation with modern software like R. We extend the model class by allowing for panel observations, i.e. clustered data with repeated measurements taken at the same individual. A random (individual) effect is incorporated in the model which accounts for the dependence structure in the data. We fit expectile sheets, meaning that not a single expectile is estimated but a whole range of expectiles is estimated simultaneously. The presented model allows for multiple covariates, where a semi-parametric approach with penalized splines is pursued to fit smooth expectile curves. We apply our methods to panel data from the German Socio-Economic Panel.