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Error-dependent smoothing rules in local linear regression
Ming Yen Cheng
, Peter Hall
Department of Mathematics
Research output
:
Contribution to journal
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Journal article
›
peer-review
4
Citations (Scopus)
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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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Keyphrases
Error Distribution
100%
Local Linear Regression
100%
Mean Squared Error
66%
Normal Error
66%
Reduction Method
33%
Asymptotic Variance
33%
Smoothing Methods
33%
Absolute Value
33%
Bias Reduction
33%
Variance Contribution
33%
Class Function
33%
Local Linear
33%
Optimal Power
33%
Minimax Lower Bound
33%
Heavy Tails
33%
Asymmetric Errors
33%
First Order Effects
33%
Double Exponential
33%
Error Performance
33%
Symmetric Errors
33%
Nonlinear Estimator
33%
Local Linear Method
33%
Mathematics
Linear Regression Analysis
100%
Error Distribution
100%
Variance
66%
Squared Error
66%
Residuals
33%
Asymptotic Variance
33%
Asymmetric
33%
Minimax
33%
Bias Reduction
33%
Absolute Value
33%
Heavy Tail
33%
Nonlinear Estimator
33%