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Detecting change points and hormone ...
~
Yang, Yu-Chieh.
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Detecting change points and hormone pulses using partial spline models.
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Detecting change points and hormone pulses using partial spline models./
Author:
Yang, Yu-Chieh.
Description:
179 p.
Notes:
Chair: Yuedong Wang.
Contained By:
Dissertation Abstracts International63-06B.
Subject:
Biology, Biostatistics. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3056037
ISBN:
9780493712468
Detecting change points and hormone pulses using partial spline models.
Yang, Yu-Chieh.
Detecting change points and hormone pulses using partial spline models.
- 179 p.
Chair: Yuedong Wang.
Thesis (Ph.D.)--University of California, Santa Barbara, 2002.
This dissertation studies partial smoothing spline models which assume the signal in the mean function has two parts: a parametric component and a nonparametric component. These models are motivated by applications in which some special features of interest can be modelled parametrically while keeping non-central features unspecified. Examples are detection of change-points problem (Muller 1992, Shiau 1999, Qiu & Yandell 1998), and pulse identification with smooth baseline in hormonal secretion (Keenan, Veldhuis & Yang 1998, Guo, Wang & Brown 1999). Other potential applications can be found in concentration mixture models in gas chromatography (Hunt 1998), and coseismic changes detection of underground water level in earthquake studies (Kitagawa & Matsumoto 1996).
ISBN: 9780493712468Subjects--Topical Terms:
1018416
Biology, Biostatistics.
Detecting change points and hormone pulses using partial spline models.
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Yang, Yu-Chieh.
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Detecting change points and hormone pulses using partial spline models.
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179 p.
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Chair: Yuedong Wang.
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Source: Dissertation Abstracts International, Volume: 63-06, Section: B, page: 2892.
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Thesis (Ph.D.)--University of California, Santa Barbara, 2002.
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This dissertation studies partial smoothing spline models which assume the signal in the mean function has two parts: a parametric component and a nonparametric component. These models are motivated by applications in which some special features of interest can be modelled parametrically while keeping non-central features unspecified. Examples are detection of change-points problem (Muller 1992, Shiau 1999, Qiu & Yandell 1998), and pulse identification with smooth baseline in hormonal secretion (Keenan, Veldhuis & Yang 1998, Guo, Wang & Brown 1999). Other potential applications can be found in concentration mixture models in gas chromatography (Hunt 1998), and coseismic changes detection of underground water level in earthquake studies (Kitagawa & Matsumoto 1996).
520
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There are three parts in the dissertation. The first part focuses on the linear semiparametric model: in the context of detection of multiple change-points, the second part focuses on nonlinear semiparametric model: pulses detection in hormonal secretions and the third part is the software implementation for ridge regression in which we extend the selection method of smoothing parameter to generalized maximal likelihood and unbiased risk score. We also extend the single parameter case to the multiple smoothing parameters case in ridge regression.
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School code: 0035.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3056037
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