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dc.contributor.author吳宜靜en_US
dc.contributor.authorWu, Yi-Jingen_US
dc.contributor.author王秀瑛en_US
dc.contributor.author吳謂勝en_US
dc.contributor.authorWang, Hsiu-Yingen_US
dc.contributor.authorWu, Wei-Shengen_US
dc.date.accessioned2014-12-12T01:50:14Z-
dc.date.available2014-12-12T01:50:14Z-
dc.date.issued2010en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT079826513en_US
dc.identifier.urihttp://hdl.handle.net/11536/47677-
dc.description.abstract生物晶片數據分析在生物學研究已被廣泛應用,然而在生物晶片中常會有遺失值的問題,往往會影響分析結果。由於許多後續分析都需要完整的數據資料,因此在生物晶片分析中,估計遺失值成為一個重要的預先處理步驟。在現今使用的遺失值估計方法中,以利用迴歸分析為基礎的估計方法最常被使用。後來為了改進估計遺失值的準確度,因此發展出許多演算法。在我們的研究中,提出了James-Stein型改進估計中迴歸係數的方法。我們利用多筆生物晶片資料比較了傳統估計法與利用James-Stein型調整方法的表現,我們可以發現James-Stein型調整方法可以有效改進傳統方法,因此我們認為這是一個更有效估計遺失值的方法。zh_TW
dc.description.abstractMicroarray data analysis has widely used in biological studies. However, it is common that there are missing values in microarray data, which affects the result of analysis. As many downstream analysis methods require complete datasets, missing value estimation has been an important pre-processing step in the microarray analysis. Among the existed missing value imputation methods, the regression-based methods are very popular. Many algorithms are developed for reconstructing these missing values. In this study, we propose a James-Stein type modified estimator for the regression coefficients. We compare the performance of the conventional imputations and the James-Stein type adjusted imputation method, our approach shows better performance than the others on various datasets.en_US
dc.language.isoen_USen_US
dc.subject遺失值估計zh_TW
dc.subjectJames-Stein估計量zh_TW
dc.subjectmissing value estimationen_US
dc.subjectJames-Stein estimatoren_US
dc.title生物晶片遺失值的最小平方差收縮插補估計法zh_TW
dc.titleA Shrinkage Least Square Imputation Method for Microarray Missing Value Estimationen_US
dc.typeThesisen_US
dc.contributor.department統計學研究所zh_TW
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