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Statistical inference on the significance of rows and columns for matrix-valued data in an additive model.
- Source :
- TEST; Sep2023, Vol. 32 Issue 3, p785-828, 44p
- Publication Year :
- 2023
-
Abstract
- Matrix-valued data arise in many applications. In this paper, we consider the setting where one collects both a matrix-valued data Y ∈ R p × q and a generic scalar X that can be continuous, discrete or categorical. Since the rows and columns of Y often have specific meanings in practice, it is interesting to make statistical inferences on the significance of rows and columns of Y . In this paper, by taking into account the background effect, we propose a new measure on significance of rows and columns based on an additive model. The point estimates, hypothesis testings and confidence intervals of the significance of a given row or column of Y are considered. Moreover, a procedure is proposed to select significant rows and columns. Our method is applicable to both p and q being much larger than sample size n. Simulation results and real data analysis demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 11330686
- Volume :
- 32
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- TEST
- Publication Type :
- Academic Journal
- Accession number :
- 173052914
- Full Text :
- https://doi.org/10.1007/s11749-023-00852-3