| dc.contributor.advisor | 江振東 | zh_TW |
| dc.contributor.advisor | Chiang, Jeng Tung | en_US |
| dc.contributor.author (Authors) | 費詩元 | zh_TW |
| dc.contributor.author (Authors) | Fei, Shih Yuan | en_US |
| dc.creator (作者) | 費詩元 | zh_TW |
| dc.creator (作者) | Fei, Shih Yuan | en_US |
| dc.date (日期) | 2008 | en_US |
| dc.date.accessioned | 2009-09-14 | - |
| dc.date.available | 2009-09-14 | - |
| dc.date.issued (上傳時間) | 2009-09-14 | - |
| dc.identifier (Other Identifiers) | G0096354010 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/30927 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 96354010 | zh_TW |
| dc.description (描述) | 97 | zh_TW |
| dc.description.abstract (摘要) | 多數關於願付價格(WTP)之研究中，遺漏資料通常被視為完全隨機遺漏(MCAR)並刪除之。然而，研究中的某些重要變數若具有過高的遺漏比例時，則可能造成分析上的偏誤。 收入在許多條件評估(Contingent Valuation)調查中經常扮演著一個重要的角色，同時其也是受訪者最傾向於遺漏的變項之一。在這份研究中，我們將透過模擬的方式來評估多重插補法(Multiple Imputa- tion) 於插補願付價格調查中之遺漏收入之表現。我們考慮三種資料情況：刪除遺漏資料後所剩餘之完整資料、一次插補資料、以及多重插補資料，針對這三種情況，藉由三要素混合模型(Three-Component Mixture Model)所進行之分析來評估其優劣。模擬結果顯示，多重插補法之分析結果優於僅利用刪除遺漏資料所剩餘之完整資料進行分析之結果，並且隨著遺漏比例上升，其優劣更是明顯。我們也發現多重插補法之結果也比起一次插補來的更加可靠、穩定。因此如果資料遺漏機制非完全隨機遺漏之機制時，我們認為多重插補法是一個值得信任且表現不錯的處理方法。 此外，文中也透過「竹東及朴子地區心臟血管疾病長期追蹤研究」(Cardio Vascular Disease risk FACtor Two-township Study，簡稱CVDFACTS) 之資料來進行實證分析。文中示範一些評估遺漏機制的技巧，包括比較存活曲線以及邏輯斯迴歸。透過實證分析，我們發現插補前後的確造成模型分析及估計上的差異。 | zh_TW |
| dc.description.abstract (摘要) | Most often, studies focus on willingness to pay (WTP) simply ignore the missing values and treat them as if they were missing completely at random. It is well-known that such a practice might cause serious bias and lead to incorrect results. Income is one of the most influential variables in CV (contingent valuation) study and is also the variable that respondents most likely fail to respond. In the present study, we evaluate the performance of multiple imputation (MI) on missing income in the analysis of WTP through a series of simulation experiments. Several approaches such as complete-case analysis, single imputation, and MI are considered and com-pared. We show that performance with MI is always better than complete-case analy-sis, especially when the missing rate gets high. We also show that MI is more stable and reliable than single imputation. As an illustration, we use data from Cardio Vascular Disease risk FACtor Two-township Study (CVDFACTS). We demonstrate how to determine the missing mechanism through comparing the survival curves and a logistic regression model fitting. Based on the empirical study, we find that discarding cases with missing in-come can lead to something different from that with multiple imputation. If the dis-carded cases are not missing complete at random, the remaining samples will be biased. That can be a serious problem in CV research. To conclude, MI is a useful method to deal with missing value problems and it should be worthwhile to give it a try in CV studies. | en_US |
| dc.description.tableofcontents | 1 Introduction 1 2 Literature Review 3 2.1 Double Bounded Dichotomous Choice Elicitation Method 3 2.2 Three-Component Mixture Model 6 2.3 Dealing Missing Covariate with WTP 6 2.4 Missing Data Mechanism 7 2.5 Multiple Imputation Method 8 3 Preliminary Data Analysis 12 4 Model Specifications 21 4.1 Three-Component Mixture Model 21 4.2 Accelerated Failure Time Model 24 5 Simulation Studies 26 5.1 Data Generation 26 5.2 Missing Value Generation 30 5.3 Evaluation 31 5.4 Results 31 6 Empirical Results 36 6.1 Variable Selection 36 6.2 Imputed-Data Analysis 38 6.3 Model Selection and Estimation 40 6.4 Complete-Case Analysis 44 6.5 Evaluation on Performance of Multiple Imputation 45 6.6 An Alternative Method to Evaluate Multiple Imputation 48 6.7 Estimation of Mean and Median after MI 51 6.8 Stability of MI 53 6.9 Simulation for Verification 56 7 Conclusion 61 Reference 63 Appendix 67 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0096354010 | en_US |
| dc.subject (關鍵詞) | 遺漏 | zh_TW |
| dc.subject (關鍵詞) | 多重插補法 | zh_TW |
| dc.subject (關鍵詞) | 願付價格 | zh_TW |
| dc.subject (關鍵詞) | Missing Value | en_US |
| dc.subject (關鍵詞) | multiple imputation | en_US |
| dc.subject (關鍵詞) | WTP | en_US |
| dc.title (題名) | 變數遺漏值的多重插補應用於條件評估法 | zh_TW |
| dc.title (題名) | Multiple imputation for missing covariates in contingent valua-tion survey | en_US |
| dc.type (資料類型) | thesis | en |
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