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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 杜憶萍(I-Ping Tu) | |
dc.contributor.author | Yi Hsiao | en |
dc.contributor.author | 蕭奕 | zh_TW |
dc.date.accessioned | 2021-05-12T09:34:11Z | - |
dc.date.available | 2020-07-02 | |
dc.date.available | 2021-05-12T09:34:11Z | - |
dc.date.copyright | 2019-07-02 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2019-05-27 | |
dc.identifier.citation | [1] Shinto Eguchi Akifumi Notsu. Robust clustering method in the presence of scattered observations. Neural Computation, 28(6):1141–1162, 2016.
[2] PeterJ.Huber.Robustestimationofalocationparameter.TheAnnalsofMathematical Statistics, 35(1):73–101, 1964. [3] Michael P. Windham. Robustifying model fitting. Journal of the Royal Statistical Society. Series B (Methodological), 57(3):599–609, 1995. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/1203 | - |
dc.description.abstract | Windham在1995年的論文Robustifying Model Fitting提出權重分布方法,解決在存有異常數據情況下可有效的估計均值,這個權重分布使用了一個參數,這個參數會影響到均值的估計表現。Windham同時提出此參數的選取方法,但我們發現在一些數值模擬例子中,這個參數選取方法表現並不佳。我們提出另一個選取方法,在數值模擬上有較佳的表現。除了一般的均值估計,我們也成功地把此方法應用在群聚分析。 | zh_TW |
dc.description.abstract | In 1995, Windham came out with an idea of weighted distribution in his thesis, Robustifying Model Fitting, and he used the idea to find a mean estimator when there are outliers in the original data. There is a tuning parameter in this estimator, and selecting the parameter will affect the mean estimate in the same data. In the same thesis, he also suggested a criterion of selecting the tuning parameter, but we found out that this criterion wasn’t doing well in some simulations. Considering the problem, we propose another criterion which can derive a better mean estimator. Besides, we can also apply this method to clustering problem. | en |
dc.description.provenance | Made available in DSpace on 2021-05-12T09:34:11Z (GMT). No. of bitstreams: 1 ntu-107-R05246014-1.pdf: 1322277 bytes, checksum: 288717b626d751660d6e4a15006119d0 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員會審定書 iii
誌謝 v Acknowledgements vii 摘要 ix Abstract xi 1 Introduction 1.1 What is Robust?............................... 1 1.2 How to get Robust?............................. 3 1.3 Why need to do this question? ....................... 6 2 Literature Review 2.1 Normal Robust Model ........................... 9 2.2 GeneralDescription............................. 10 2.3 SelectionCriterion ............................. 12 2.4 γ-estimate and Weighted Robustified estimate . . . . . . . . . 14 3 Our Selection Criterion 3.1 gθ is univariate normal,θ=μ ....................... 17 3.2 gθ is bivariate normal,θ=μ ........................ 18 3.3 gθ is univariate normal,θ=(μ,σ2)T ................... 19 3.4 gθ is qGaussian,θ=μ ........................... 21 4 Numerical Examples 4.1 One Dimension Case ............................ 27 4.2 Two Dimension-One Component with Outliers . . . . . . . . . . 31 4.3 VarianceUnknown ............................. 32 4.4 qGaussianModel .............................. 33 Bibliography 37 | |
dc.language.iso | en | |
dc.title | 建立在伽瑪散度下穩健模型的調整參數之選取 | zh_TW |
dc.title | Robust Model Fitting - Selection of Tuning Parameters in the Aspect of Gamma Clustering | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳素雲(Su-Yun Huang),姚怡慶(Yi-Ching Yao),陳定立(Ting-Li Chen),陳宏(Hung Chen) | |
dc.subject.keyword | 穩健估計,影響函數,伽瑪散度,群聚分析, | zh_TW |
dc.subject.keyword | robust estimate,influence function,gamma-divergence,clustering, | en |
dc.relation.page | 37 | |
dc.identifier.doi | 10.6342/NTU201900783 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2019-05-28 | |
dc.contributor.author-college | 理學院 | zh_TW |
dc.contributor.author-dept | 應用數學科學研究所 | zh_TW |
顯示於系所單位: | 應用數學科學研究所 |
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