论文标题
减少大规模协方差回归的子空间模型
Reducing Subspace Models for Large-Scale Covariance Regression
论文作者
论文摘要
我们为大型$ p $，小$ n $设置的联合平均值和协方差回归开发了一个信封模型。与现有的包络方法相反，该方法通过结合协方差结构的估计来改善平均估计值，我们专注于通过合并有关均值级别差异的信息来识别协方差异质性。我们使用蒙特卡洛EM算法来识别低维子空间，该子空间解释了均值和协方差的差异，这是协变量的函数，然后使用MCMC估计在推断的低维子空间上的后端不确定性条件。我们证明了模型对衰老代谢组学的激励应用的实用性。我们还提供R代码，可用于开发和测试响应信封模型的其他概括。
We develop an envelope model for joint mean and covariance regression in the large $p$, small $n$ setting. In contrast to existing envelope methods, which improve mean estimates by incorporating estimates of the covariance structure, we focus on identifying covariance heterogeneity by incorporating information about mean-level differences. We use a Monte Carlo EM algorithm to identify a low-dimensional subspace which explains differences in both means and covariances as a function of covariates, and then use MCMC to estimate the posterior uncertainty conditional on the inferred low-dimensional subspace. We demonstrate the utility of our model on a motivating application on the metabolomics of aging. We also provide R code which can be used to develop and test other generalizations of the response envelope model.
