A1003
Title: Robust covariance matrix estimation for high-dimensional compositional data with application to sales data analysis
Authors: Danning Li - Northeast Normal University (China) [presenting]
Abstract: 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. The aim is to propose a robust composition-adjusted thresholding covariance procedure based on Huber-type M-estimation to estimate the sparse covariance structure of high-dimensional compositional data. A cross-validation procedure is introduced to choose the tuning parameters of the proposed method. Theoretically, by assuming a bounded fourth-moment condition, the rates of convergence and signal recovery property for the proposed method are obtained, and the theoretical guarantees are provided for the cross-validation procedure under the high-dimensional setting. Numerically, the effectiveness of the proposed method is demonstrated in simulation studies and also a real application to sales data analysis.
