- Title
- New two-stage method for nonparametric regression with jump points
- Creator
- Wu, Changzhi; Liu, C; Teo, Kok Lay; Shao, Q
- Date
- 2008
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/103097
- Identifier
- vital:10881
- Identifier
- ISBN:978-981-279-056-9
- Identifier
- https://espace.curtin.edu.au/handle/20.500.11937/19212
- Abstract
- In this chapter, a two-staged method is presented for nonparametric regression with jump points. After the rough location of all the possible jump points are identified using the existing efficient kernel method, a smoothing spline function is used to approximate each segment of the regression function. A time scaling transformation is derived so as to map the undecided jump points into fixed points. This approximation problem is formulated as an optimization problem which can be solved by many existing techniques. The method is applied to several examples. The results obtained show that the method is highly efficient
- Publisher
- World Scientific Publishing
- Relation
- Control of the chaos in nonlinear circuits and systems Chapter 4 p. 79-94
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
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