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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 歐陽彥正 | |
| dc.contributor.author | Cheng-En Hong | en |
| dc.contributor.author | 洪晟恩 | zh_TW |
| dc.date.accessioned | 2021-06-08T02:51:53Z | - |
| dc.date.available | 2030-01-01 | - |
| dc.date.copyright | 2017-08-24 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-14 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20526 | - |
| dc.description.abstract | 在實際的問題中儘管擁有許多的變數，我們並不曉得哪些變數是
真實的變數，哪些是虛假的雜訊。通過發現重要變數，研究人員可以 進一步利用選擇的重要變數進行更有針對性的後續實驗以利探討背 後的科學現象。一個自然的要求是，我們希望盡可能發現更多的相 關變量，同時盡可能犯更少的錯誤。我們提出一個改良的RuleFit 模 型，其中包含利用knockoff procedure 達到控制錯誤發現率, 以及通過 Neyman-Pearson 方法控制型一誤差。 | zh_TW |
| dc.description.abstract | Despite the abundance of the available variables, ground truth is privy
to knowledge about the problem seldom revealed in practice. By discovering important features, researchers can further conduct a more targeted follow-up experiment on the selected features tailored for understanding the scientific phenomenon. A natural requirement is that we wish to discover as many relevant variables as possible and make as few mistakes as possible at the same time. We propose a modified RuleFit with FDR control by knockoff procedure and with alpha control by Neyman-Pearson method. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T02:51:53Z (GMT). No. of bitstreams: 1 ntu-106-R04H41006-1.pdf: 1211308 bytes, checksum: 7d01f9a5e7a176550195e56f958ce3e9 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii Abstract iv Contents v List of Figures vii List of Tables viii Notations ix 1 Introduction 1 1.1 Literature Review 2 1.2 Background 7 1.2.1 RuleFit 7 1.2.2 The Lasso 9 1.3 Motivation 12 1.4 Framework of Thesis 12 2 Methods 14 2.1 Knockoff Procedure 14 2.1.1 Preliminaries and Notations 14 2.1.2 Construct Knockoffs 18 2.1.3 Calculate Feature Statistics 21 2.1.4 Calculate a Data-Dependent Threshold 25 2.1.5 Two-Stage Modification 30 2.1.6 Summary 31 2.2 Neyman-Pearson Method 31 2.2.1 Preliminaries and Notations 31 2.2.2 Neyman-Pearson Umbrella Algorithm 33 3 Results and Discussion 38 3.1 Knockoff Procedure 38 3.1.1 Knockoff Result Summary 41 3.2 Neyman-Pearson Method 41 3.2.1 Neyman-Pearson Method Result Summary 42 3.3 Real Data Analysis 45 3.3.1 Real Data Analysis Result Summary 46 4 Conclusion 47 References 49 | |
| dc.language.iso | en | |
| dc.title | 具有錯誤發現率和型一誤差控制的可解釋之預測樹模型 | zh_TW |
| dc.title | A tree-based interpretable predictive method with FDR and
type-one error control | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 韓謝忱,蔡政安 | |
| dc.subject.keyword | 模型選擇,錯誤發現率, | zh_TW |
| dc.subject.keyword | Knockoff,FDR,Lasso,Neyman-Pearson method, | en |
| dc.relation.page | 54 | |
| dc.identifier.doi | 10.6342/NTU201702789 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2017-08-14 | |
| dc.contributor.author-college | 共同教育中心 | zh_TW |
| dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
| dc.date.embargo-terms | 2030-01-01 | |
| Appears in Collections: | 統計碩士學位學程 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-106-1.pdf Restricted Access | 1.18 MB | Adobe PDF |
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