论文标题
在低级矩阵单索引模型上
On a low-rank matrix single index model
论文作者
论文摘要
在本文中，我们介绍了低级矩阵单索引模型的理论研究。最近在生物统计学中引入了该模型，但是其在估计链路函数和系数矩阵的理论特性尚未进行。在这里，我们推进了使用Pac-Bayesian Bounds技术来提供严格的理论理解，以共同估计链路函数和系数矩阵。
In this paper, we present a theoretical study of a low-rank matrix single index model. This model is recently introduced in biostatistics however its theoretical properties on estimating together the link function and the coefficient matrix are not yet carried out. Here, we advance on using PAC-Bayesian bounds technique to provide a rigorous theoretical understanding for jointly estimation of the link function and the coefficient matrix.
