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高校应用数学学报
Applied Mathematics A Journal of Chinese Universities  2017, Vol. 32 Issue (3): 306-314    DOI:
    
Adaptive penalized spline regression model via radial basis
DING Meng-zhen, YANG Lian-qiang, JIANG Kun, WANG Xue-jun
School of Math. Sci., Anhui Univ., Hefei 230601, China
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Abstract  Classical penalized regression model is inadequate of adaptivity for fitting complex data because that the spatial heterogeneity of observation data is not considered by the penalized term. According to the geometric meaning of radial basis, the local penalization vector based on the ranges of the data around each knot is constructed and added into the penalized term of the model. This new adaptive penalized spline regression model via radial basis gives less penalization to fitted curve where the observation data is volatile and more penalization to fitted curve where the observation data is flat, which makes the model adaptive to the local characterization of the sample points. Simulations and application show the fitting effect based on new model outperforms classical penalized spline regression model.

Key words： nonparametric regression      penalized spline      adaptivity      range     
Received: 27 December 2016      Published: 07 April 2018
CLC:  O212.7  
  O241.3  
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DING Meng-zhen
YANG Lian-qiang
JIANG Kun
WANG Xue-jun
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DING Meng-zhen, YANG Lian-qiang, JIANG Kun, WANG Xue-jun. Adaptive penalized spline regression model via radial basis. Applied Mathematics A Journal of Chinese Universities, 2017, 32(3): 306-314.

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http://www.zjujournals.com/amjcua/     OR     http://www.zjujournals.com/amjcua/Y2017/V32/I3/306


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