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Dimension folding PCA and PFC for matrix-valued predictors
Shanshan Ding, R. Dennis Cook
Statistics (Twin Cities)
Research output
:
Contribution to journal
›
Article
›
peer-review
32
Scopus citations
Overview
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Dive into the research topics of 'Dimension folding PCA and PFC for matrix-valued predictors'. Together they form a unique fingerprint.
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Mathematics
Folding
87%
Principal Components
81%
Principal Component Analysis
77%
Predictors
67%
Dimension Reduction
27%
Data Structures
24%
Information Gain
18%
Moment
16%
Simulation Analysis
16%
Slicing
16%
Robust Estimation
14%
Reduction Method
13%
Complex Structure
13%
Slice
13%
Conditional Distribution
12%
Maximum Likelihood Estimation
11%
Data analysis
11%
Model-based
11%
Asymptotic Properties
10%
Business & Economics
Principal Component Analysis
100%
Principal Components
83%
Predictors
78%
Matrix
64%
Dimension Reduction
47%
Data Structures
31%
Maximum Likelihood Estimation
22%
Robust Estimation
16%
Asymptotic Properties
15%
Conditional Distribution
14%
Simulation Analysis
14%