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Error-dependent smoothing rules in local linear regression
Ming Yen Cheng
, Peter Hall
Department of Mathematics
Research output
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Contribution to journal
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Article
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peer-review
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Dive into the research topics of 'Error-dependent smoothing rules in local linear regression'. Together they form a unique fingerprint.
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Mathematics
Local Linear Regression
100%
Smoothing
62%
Dependent
45%
Mean Squared Error
19%
Estimator
17%
Performance
17%
Local Linear Method
16%
Bias Reduction
13%
Smoothing Methods
12%
Heavy Tails
11%
Violate
11%
Error Estimator
11%
Asymptotic Variance
10%
Bandwidth
9%
Trade-offs
9%
Weighting
9%
Absolute value
9%
Minimax
9%
Proportion
8%
First-order
6%
Curve
5%
Business & Economics
Smoothing
73%
Linear Regression
70%
Estimator
27%
Mean Squared Error
20%
Bias Reduction
13%
Asymptotic Variance
11%
Heavy Tails
11%
Order Effects
10%
Minimax
10%
Performance
9%
Bandwidth
9%
Weighting
8%
Proportion
6%
Trade-offs
6%