博碩士論文 109225024 詳細資訊




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姓名 李宥序(Yu-Hsu Li)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 長期追蹤資料上的 Gamma-EM 分群
(Gamma-EM clustering on longitudinal data)
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摘要(中) 隨著現代科技和醫學的進步,已經有很多精密儀器可以準確檢測各種生物指標。在實踐中,研究一種藥物是否具有顯著效果是藥物研發中的一個重要問題。傳統上,我們會驗證實驗組和對照組是否有顯著差異,然後解釋藥物療效是否有效;然而,在一些臨床數據上,我們不知道數據背後的分群,進而判斷藥物有效性,本文將採用 PBC 資料作為例子。這裡我們使用Lin 和Wang(2021)提出的γ-EM 算法對未知群 體的種群進行聚類分析。γ-EM 是通過γ-divergence 改進的EM 算法,可用於實現分類 的魯棒性。在這種情況下,我們可以使用γ-EM 來初步了解種群是否具有不同群體的表現。
摘要(英) With the advancement of modern technology and medicine, there are already many sophisticated instruments that can accurately detect various biological indicators. In practice, it is an important issue in drug research and development to study whether a drug has a significant effect. Traditionally, we will verify whether there is a significant difference between the experimental group and the control group, and then explain whether the drug efficacy is effective; from another perspective, here we use the γ-EM algorithm proposed by Lin and Wang(2021) to perform cluster analysis on the population of unknown groups.
γ-EM is an improved EM algorithm through γ-divergence, which can be used to achieve robustness in classification. In this case, we can use γ-EM to initially understand whether the population has the performance of different groups.
關鍵字(中) ★ EM 演算法
★ 散度
★ 長期追蹤資料
★ 線性混合模型
★ 分群
關鍵字(英) ★ EM algorithm
★ divergence
★ longitudinal data
★ linear mixed effect model
★ clustering
論文目次 Contents
page
摘要iii
Abstract v
Acknowledgement vii
Contents ix
List of Figures xi
List of Tables xiii
1 Introduction 1
1.1 Longitudinal data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Classification and Clustering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Clustering of longitudinal data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Background 5
2.1 Linear mixed effect model (LMM). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Maximum Likelihood Estimation (MLE). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Expectation-maximization (EM) algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.4 Divergence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.5 q-Gaussian Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
ix
CONTENTS
2.6 Density-based spatial clustering of applications with noise (DBSCAN) . . . . . 12
3 Methodology 15
3.1 LMM in longitudinal clustering data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Gamma-EM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Simulation 21
4.1 Two groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1.1 Model setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1.2 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.3 Comparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.2 Three groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2.1 Model setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2.2 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2.3 Comparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5 Application 29
5.1 Primary Biliary Cirrhosis (PBC) Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5.2 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6 Conclusion 35
Bibliography 37
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[8] James MacQueen et al. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1, pages 281–297. Oakland, CA, USA, 1967.
[9] Wim Meeus, Jurjen Iedema, Marianne Helsen, and Wilma Vollebergh. Patterns of adolescent identity development: Review of literature and longitudinal analysis.
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[10] Giovanni Saraceno, Abhik Ghosh, Ayanendranath Basu, and Claudio Agostinelli. Robust estimation under linear mixed models: The minimum density power divergence approach. arXiv preprint arXiv:2010.05593, 2020.
[11] Jean D Skinner, Betty Ruth Carruth, Wendy Bounds, and Paula J Ziegler. Children’s food preferences: a longitudinal analysis. Journal of the American Dietetic Association, 102(11):1638–1647, 2002.
[12] Xi Zhang. Longitudinal Data Clustering Via Kernel Mixture Models. PhD thesis, 2021.
指導教授 王紹宣(Shao-Hsuan Wang) 審核日期 2022-8-1
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