| DC 欄位 |
值 |
語言 |
| DC.contributor | 統計研究所 | zh_TW |
| DC.creator | 徐瑋辰 | zh_TW |
| DC.creator | Wei-Chern Hsu | en_US |
| dc.date.accessioned | 2020-7-30T07:39:07Z | |
| dc.date.available | 2020-7-30T07:39:07Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=107225021 | |
| dc.contributor.department | 統計研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 在醫學研究中，生物指標因素(prognosis factor)和其相對應的預測模
型已經被廣泛使用。存活樹(Survival tree)和森林(Survival forest)是當
前非常熱門用於存活數據(Survival data)開發預測模型的非參數方法。它們
具有很高的彈性，可以合理地檢測某些變數間的交互作用而不需要太多模型
假設。此外，一棵存活樹可以根據其二元分類及不斷遞迴的特性產生多個指
標因素並將樣本分為多個組別。在本文中，我們點名的存活樹在高維度變數
下的實施困難原因及解決辦法。此外，我們還指出，用於檢測樹節點在傳統
logrank test 下具有致命的缺點。為了解決上述問題，我們提出了穩定單變
量score statistics 來找出樹的節點。進階來說，我們可以在沒有任何迭代
優化的情況下執行高維度變數的篩選和提出決策，在某些特殊運算下能提升
效率。本文也提出對於當logrank test 無法提供適量的統計決策時，我們提
出的方法能適當解決這個問題並產生更有預測能力的存活樹。 | zh_TW |
| dc.description.abstract | Analysis of prognostic factors and prediction models has been considered extensively in
medical research. Survival trees and forests are popular non-parametric tools for developing
prognostic models for survival data. They offer great flexibility and can automatically detect
certain types of interactions without the need to specify them beforehand. Moreover, a single tree
can naturally classify subjects into different groups according to their survival prognosis based on
their covariates. In this thesis, we point out the difficulty of tree-based model fitting a high
dimensional covariate. Furthermore, we also point out that the traditional logrank tests for
detecting the nodes of a tree have fatal drawbacks. In order to overcome these difficulties, we
propose a stabilized univariate score statistics to find the nodes of a tree. We show that the high
dimensional score tests can be performed without any iteration and optimization, leading to a
computationally efficient test procedures. We also show that the proposed method can resolve the
drawbacks of the logrank tests, leading to a highly precise tree. Simulation studies are performed
to see the relative performance of the proposed method with the existing method. The lung cancer
dataset is analyzed for illustration. | en_US |
| DC.subject | 右設限 | zh_TW |
| DC.subject | 樹 | zh_TW |
| DC.subject | 高維度變數 | zh_TW |
| DC.subject | 基因序列 | zh_TW |
| DC.subject | Right censoring | en_US |
| DC.subject | Tree | en_US |
| DC.subject | High dimensional covariate | en_US |
| DC.subject | Gene selection | en_US |
| DC.title | A Survival Tree based on Stabilized Univariate Score Tests with High Dimensional Covariates | en_US |
| dc.language.iso | en_US | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |