• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics and computing >Functional data analysis of generalized regression quantiles
【24h】

Functional data analysis of generalized regression quantiles

机译：广义回归分位数的功能数据分析

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations.
机译：广义回归分位数，包括作为特殊情况的条件分位数和期望分位数，是表征条件分布的条件方法的有用替代方法，尤其是当关注点在尾巴上时。我们开发了一种功能数据分析方法，以共同估算一系列广义回归分位数。我们的方法假设广义回归分位数具有一些共同的特征，这些共同特征可以由少量的主成分函数来概括。主成分函数建模为样条曲线，并通过最小化惩罚性非对称损耗度量进行估算。开发了迭代最小不对称加权平方算法进行计算。尽管由于缺少足够的数据而对单个广义回归分位数进行单独估计通常会出现较大的可变性，但通过跨数据集的借用强度，我们的联合估计方法显着提高了估计效率，这在仿真研究中得到了证明。将该方法应用于中国159个气象站的数据，获得了这些气象站温度波动的广义分位数曲线。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号