Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis
针对高维成分数据，提出一种基于Huber型M估计的稳健协方差估计方法，无需高斯或次高斯假设，通过交叉验证选择参数，理论证明在四阶矩有界条件下的收敛性，模拟和销售数据应用验证了有效性。
Compositional data arises in a wide variety of research areas when some form of standardization and composition is necessary. Estimating covariance matrices is of fundamental importance for high-dimensional compositional data analysis. However, existing methods require the restrictive Gaussian or sub-Gaussian assumption, which may not hold in practice. We propose a robust composition adjusted thresholding covariance procedure based on Huber-type <i>M</i>-estimation to estimate the sparse covariance structure of high-dimensional compositional data. We introduce a cross-validation procedure to choose the tuning parameters of the proposed method. Theoretically, by assuming a bounded fourth moment condition, we obtain the rates of convergence and signal recovery property for the proposed method and provide the theoretical guarantees for the cross-validation procedure under the high-dimensional setting. Numerically, we demonstrate the effectiveness of the proposed method in simulation studies and also a real application to sales data analysis.