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Kernel spline regression
Author(s) -
Braun W. John,
Huang LiShan
Publication year - 2005
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550330207
Subject(s) - polynomial regression , kernel regression , mathematics , spline (mechanical) , smoothing spline , polynomial kernel , kernel smoother , kernel (algebra) , nonparametric regression , local regression , variable kernel density estimation , principal component regression , estimator , kernel principal component analysis , statistics , regression analysis , kernel method , radial basis function kernel , computer science , spline interpolation , artificial intelligence , support vector machine , combinatorics , engineering , bilinear interpolation , structural engineering
Abstract The authors propose «kernel spline regression,» a method of combining spline regression and kernel smoothing by replacing the polynomial approximation for local polynomial kernel regression with the spline basis. The new approach retains the local weighting scheme and the use of a bandwidth to control the size of local neighborhood. The authors compute the bias and variance of the kernel linear spline estimator, which they compare with local linear regression. They show that kernel spline estimators can succeed in capturing the main features of the underlying curve more effectively than local polynomial regression when the curvature changes rapidly. They also show through simulation that kernel spline regression often performs better than ordinary spline regression and local polynomial regression.