Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis-Reference-Cited by-同舟云学术

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

Published:2022-09-21 Issue: Volume: Page:1-11
ISSN:0735-0015
Container-title:Journal of Business & Economic Statistics
language:en
Short-container-title:Journal of Business & Economic Statistics

Author:

Li Danning¹,Srinivasan Arun²,Chen Qian³,Xue Lingzhou²^ORCID

Affiliation:

1. School of Mathematics and Statistics and KLAS, Northeast Normal University, Changchun, China

2. Department of Statistics, Penn State University, University Park, PA

3. Department of Supply Chain and Information Systems, Penn State University, University Park, PA

Funder

Division of Mathematical Sciences

National Institute of Allergy and Infectious Diseases

the National Natural Science Foundation of China

Arun Srinivasan and Lingzhou Xue

National Institutes of Health

the National Science Foundation

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/07350015.2022.2106990

Reference27 articles.

1. The Statistical Analysis of Compositional Data

2. Principal component analysis of compositional data

3. Log contrast models for experiments with mixtures

4. Scale Mixtures of Normal Distributions

5. Arata, Y., and Onozaki, T. (2017), “A Compositional Data Analysis of Market Share Dynamics,” Discussion papers, Research Institute of Economy, Trade and Industry (RIETI). Available at: https://EconPapers.repec.org/RePEc:eti:dpaper:17076.

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