论文标题
多元功能响应低等级回归，并应用于脑成像数据
Multivariate functional responses low rank regression with an application to brain imaging data
论文作者
论文摘要
我们提出了一个多元功能响应低级回归模型，具有可能的高维功能响应和标量协变量。通过以一组筛子的基础扩展斜率函数，我们将基础系数重建为矩阵。为了估计这些系数，我们提出了使用核规范正则化的有效程序。我们还为我们的估计得出了错误界限，并使用模拟评估了我们的方法。我们进一步将我们的方法应用于人类Connectome项目神经成像数据，以预测使用各种临床协变量的皮质表面运动诱发的功能磁共振成像信号，以说明我们结果的有用性。
We propose a multivariate functional responses low rank regression model with possible high dimensional functional responses and scalar covariates. By expanding the slope functions on a set of sieve basis, we reconstruct the basis coefficients as a matrix. To estimate these coefficients, we propose an efficient procedure using nuclear norm regularization. We also derive error bounds for our estimates and evaluate our method using simulations. We further apply our method to the Human Connectome Project neuroimaging data to predict cortical surface motor task-evoked functional magnetic resonance imaging signals using various clinical covariates to illustrate the usefulness of our results.
