Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1171-1186 |
| Number of pages | 16 |
| Journal | Communications in Statistics: Simulation and Computation |
| Volume | 44 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2015 May 7 |
| Externally published | Yes |
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Keywords
- Change point
- Nonparametric regression
- Photodynamic therapy
- Reproducing kernel Hilbertspace
- Spline.
ASJC Scopus subject areas
- Statistics and Probability
- Modelling and Simulation
Cite this
An efficient operator for the change point estimation in partial spline model. / Han, Sung Won; Zhong, Hua; Putt, Mary.
In: Communications in Statistics: Simulation and Computation, Vol. 44, No. 5, 07.05.2015, p. 1171-1186.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An efficient operator for the change point estimation in partial spline model
AU - Han, Sung Won
AU - Zhong, Hua
AU - Putt, Mary
PY - 2015/5/7
Y1 - 2015/5/7
N2 - 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.
AB - 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.
KW - Change point
KW - Nonparametric regression
KW - Photodynamic therapy
KW - Reproducing kernel Hilbertspace
KW - Spline.
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UR - http://www.scopus.com/inward/citedby.url?scp=84908638616&partnerID=8YFLogxK
U2 - 10.1080/03610918.2013.809103
DO - 10.1080/03610918.2013.809103
M3 - Article
VL - 44
SP - 1171
EP - 1186
JO - Communications in Statistics Part B: Simulation and Computation
JF - Communications in Statistics Part B: Simulation and Computation
SN - 0361-0918
IS - 5
ER -