An Efficient Operator for the Change Point Estimation in Partial Spline Model

被引:0
作者
Han, Sung Won [1 ]
Zhong, Hua [1 ]
Putt, Mary [2 ]
机构
[1] NYU, Dept Populat Hlth, New York, NY 10012 USA
[2] Univ Penn, Dept Biostat & Epidemiol, Philadelphia, PA 19104 USA
关键词
Change point; Nonparametric regression; Photodynamic therapy; Reproducing kernel Hilbertspace; Spline; 62; 62G08; SMOOTHING PARAMETER; REGRESSION; INFORMATION; SELECTION;
D O I
10.1080/03610918.2013.809103
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In bioinformatics application, the estimation of the starting and ending points of drop-down in the longitudinal data is important. One possible approach to estimate such change times is to use the partial spline model with change points. In order to use estimate change time, the minimum operator in terms of a smoothing parameter has been widely used, but we showed that the minimum operator causes large MSE of change point estimates. In this paper, we proposed the summation operator in terms of a smoothing parameter, and our simulation study showed that the summation operator gives smaller MSE for estimated change points than the minimum one. We also applied the proposed approach to the experiment data, blood flow during photodynamic cancer therapy.
引用
收藏
页码:1171 / 1186
页数:16
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