函数卷积模型的可辨识性
On the Identifiability of the Functional Convolution Model
摘要：
本报告详细介绍了在不降低维数或平滑损失的情况下，可从普通最小二乘估计识别\citet{AHG13}中所述的函数卷积模型的条件。证明了若协变量函数不被线性微分方程的解空间所张成，则模型中的函数系数在二阶导数绝对连续的函数的索伯列夫空间中唯一确定.
英文摘要：
This report details conditions under which the Functional Convolution Model described in \citet{AHG13} can be identified from Ordinary Least Squares estimates without either dimension reduction or smoothing penalties. We demonstrate that if the covariate functions are not spanned by the space of solutions to linear differential equations, the functional coefficients in the model are uniquely determined in the Sobolev space of functions with absolutely continuous second derivatives.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
