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On surrogate dimension reduction for measurement error regression: An invariance law
Bing Li
, Xiangrong Yin
Statistics
Research output
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Contribution to journal
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Article
›
peer-review
16
Scopus citations
Overview
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Dive into the research topics of 'On surrogate dimension reduction for measurement error regression: An invariance law'. Together they form a unique fingerprint.
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Mathematics
Surrogate
62%
Measurement Error
55%
Invariance
44%
Regression
39%
Predictors
31%
Reduction Method
29%
Consistent Estimates
10%
Nonlinear Regression
10%
Multivariate Normal
9%
Two Dimensions
7%
Equivalence
5%
Imply
5%
Estimator
5%
Performance
5%
Arbitrary
4%
Business & Economics
Dimension Reduction
100%
Measurement Error
81%
Invariance
60%
Predictors
36%
Nonlinear Regression
11%
Equivalence
8%
Estimator
6%
Performance
3%