Bayesian Curve Registration and Classification
碩士 === 國立政治大學 === 統計研究所 === 97 === Functional data are random curves observed in a period of time at discrete time points.They often exhibit a common shape, but with variations in amplitude and phase across curves.To estimate the common shape,some adjustment for synchronization is often made,which i...
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ndltd-TW-097NCCU53370082015-11-20T04:18:48Z http://ndltd.ncl.edu.tw/handle/05764398775307610056 Bayesian Curve Registration and Classification 貝氏曲線同步化與分類 Lee,Po- Hung 李柏宏 碩士 國立政治大學 統計研究所 97 Functional data are random curves observed in a period of time at discrete time points.They often exhibit a common shape, but with variations in amplitude and phase across curves.To estimate the common shape,some adjustment for synchronization is often made,which is also known as time warping or curve registration.In this thesis,splines are used to model the warping functions and the common shape. Certain parameters are allowed to be random.For the estimation of the random parameters,priors are proposed so that samples from the posteriors can be obtained using Markov chain Monte Carlo methods.For the estimation of non-random parameters, a penalized likelihood approach is used. It is found via simulation studies that for a set of random curves with a common shape,the estimated common shape function looks like the true function up to a location-scale transform,and the curve alignment based on estimated time warping functions looks reasonable.For two groups of random curves which differ in the group common shape functions,synchronization also improves the discrimination between groups in some cases. Key words: functional data analysis,curve registration,curve discrimination,markov chain monte carlo method. Huang, Tzee-Ming 黃子銘 2009 學位論文 ; thesis 38 zh-TW |
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碩士 === 國立政治大學 === 統計研究所 === 97 === Functional data are random curves observed in a period of time at discrete time points.They often exhibit a common shape, but with variations in amplitude and phase across curves.To estimate the common shape,some adjustment for synchronization is often made,which is also known as time warping or curve registration.In this thesis,splines are used to model the warping functions and the common shape. Certain parameters are allowed to be random.For the estimation of the random parameters,priors are proposed so that samples from the posteriors can be obtained using Markov chain Monte Carlo methods.For the estimation of non-random parameters, a penalized likelihood approach is used. It is found via simulation studies that for a set of random curves with a common shape,the estimated common shape function looks like the true function up to a location-scale transform,and the curve alignment based on estimated time warping functions looks reasonable.For two groups of random curves which differ in the group common shape functions,synchronization also improves the discrimination between groups in some cases.
Key words: functional data analysis,curve registration,curve discrimination,markov chain monte carlo method.
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Huang, Tzee-Ming |
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Huang, Tzee-Ming Lee,Po- Hung 李柏宏 |
author |
Lee,Po- Hung 李柏宏 |
spellingShingle |
Lee,Po- Hung 李柏宏 Bayesian Curve Registration and Classification |
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Lee,Po- Hung |
title |
Bayesian Curve Registration and Classification |
title_short |
Bayesian Curve Registration and Classification |
title_full |
Bayesian Curve Registration and Classification |
title_fullStr |
Bayesian Curve Registration and Classification |
title_full_unstemmed |
Bayesian Curve Registration and Classification |
title_sort |
bayesian curve registration and classification |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/05764398775307610056 |
work_keys_str_mv |
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