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

Danning Li, Arun Srinivasan, Qian Chen, Lingzhou Xue

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 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.

Original languageEnglish (US)
Pages (from-to)1090-1100
Number of pages11
JournalJournal of Business and Economic Statistics
Volume41
Issue number4
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis'. Together they form a unique fingerprint.

Cite this