在本論文中，對於無關聯問題隨機作答技巧的高維度問卷資料，我們使用的損失函數是由羅吉斯迴歸模型下的負對數概似函數來獲得，並利用機器提升學習方法來建構模型與選取變量，該方法的計算是使用 R 軟體中的"mboost"套件來進行。我們提了 3 種決定選出變量重要性的方法，也提了 1 個評估最終模型預測效能的指標。我們進行了模擬試驗展示該方法的數值表現，並分析台灣婚外情的無關聯問題隨機作答技巧之問卷資料，作為該方法的實例應用。

For high dimensional unrelated randomized response technique survey data, we, in this thesis, base on the loss function, constructed by the negative log-likelihood under the logistic model, to propose a machine boosting learning for model building and variable selection. The computation of the method is implemented by the modified R package ‘mboost’. We propose three methods to determine the importance of selected variables and on index to evaluate the predictive power of the final model. The proposed method is evaluated by simulation studies and illustrated by the analysis an extramarital sex survey dataset of Taiwan residents.

一、前言............................................................1
二、資料與模型介紹..................................................5
三、梯度提升法......................................................7
四、模擬...........................................................12
五、實例分析.......................................................17
六、結論...........................................................24
七、參考文獻.......................................................25

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