首页函数论
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在功能回归预测误差

The prediction error in functional regression
课程网址: http://videolectures.net/sip08_kneip_tpeifr/  
主讲教师: Alois Kneip
开课单位: 波恩大学
开课时间: 2008-12-18
课程语种: 英语
中文简介:
该演讲考虑了函数线性回归,其中标量响应Y是根据随机函数建模的。我们提出了一个基于通常惩罚的轻微修改的功能斜率参数的平滑样条估计器。理论分析集中于新随机函数的响应的样本外预测中的误差。结果表明,预测误差的收敛速度取决于斜率函数的平滑度和预测变量的结构。然后我们证明这些速率是最优的,因为它们是大类可能的斜率函数和预测曲线分布的极小极大值。对于具有变量误差的模型的情况,通过使用离散化曲线的协方差矩阵的去噪校正来修改平滑样条估计器。然后将该方法应用于实际案例研究,其目的是通过使用前一天测量的该浓度的曲线来预测臭氧浓度的最大值。
课程简介: The talk considers functional linear regression, where scalar responses Y are modeled in dependence of random functions. We propose a smoothing splines estimator for the functional slope parameter based on a slight modi- fication of the usual penalty. Theoretical analysis concentrates on the error in an out-of-sample prediction of the response for a new random function. It is shown that rates of convergence of the prediction error depend on the smoothness of the slope function and on the structure of the predictors. We then prove that these rates are optimal in the sense that they are minimax over large classes of possible slope functions and distributions of the predic- tive curves. For the case of models with errors-in-variables the smoothing spline estimator is modified by using a denoising correction of the covari- ance matrix of discretized curves. The methodology is then applied to a real case study where the aim is to predict the maximum of the concentration of ozone by using the curve of this concentration measured the preceding day.
关 键 词: 线性回归; 随机函数; 斜坡函数
课程来源: 视频讲座网
最后编审: 2020-06-29:wuyq
阅读次数: 180