Testing the mean matrix in high-dimensional transposable data
- Submitting institution
-
University of Brighton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 7123758
- Type
- D - Journal article
- DOI
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10.1111/biom.12257
- Title of journal
- Biometrics
- Article number
- -
- First page
- 157
- Volume
- 71
- Issue
- 1
- ISSN
- 0006-341X
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Simple statistical methods that fail to acknowledge the dependence structure in high-dimensional matrix-variate random variables, are routinely used to assess the mean relationship between the row and column features of these matrices. This paper is significant because it provides a theoretically sound hypothesis-testing procedure that can address various forms of the mean relationship while also considering the dependence structure of the row and column features. Extensive simulations demonstrate the superiority of the proposed method against the ANOVA tests, the Kruskal-Wallis test and the one sample mean test of Chen and Qin (Ann. Statist. 2010).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -