Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis.

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. We 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. 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 p

SUBMITTER: Li D 

PROVIDER: S-EPMC10730115 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature