| dc.contributor.advisor | 陳麗霞 | zh_TW |
| dc.contributor.author (Authors) | 張凱嵐 | zh_TW |
| dc.contributor.author (Authors) | Chang, Kai lan | en_US |
| dc.creator (作者) | 張凱嵐 | zh_TW |
| dc.creator (作者) | Chang, Kai lan | 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) | G0095354013 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/30922 | - |
| 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) | 95354013 | zh_TW |
| dc.description (描述) | 97 | zh_TW |
| dc.description.abstract (摘要) | 近來地理資訊系統(GIS)之資料庫受到不同領域的統計學家廣泛的研究，以期建立及分析可描述空間聚集效應及變異之模型，而描述空間相關存活資料之統計模式為公共衛生及流行病學上新興的研究議題。本文擬建立多維度半參數的貝氏階層模型，並結合空間及非空間隨機效應以描述存活資料中的空間變異。此模式將利用多變量條件自回歸(MCAR)模型以檢驗在不同地理區域中是否存有空間聚集效應。而基準風險函數之生成為分析貝氏半參數階層模型的重要步驟，本研究將利用混合Polya樹之方式生成基準風險函數。美國國家癌症研究院之「流行病監測及最終結果」(Surveillance Epidemiology and End Results, SEER)資料庫為目前美國最完整的癌症病人長期追蹤資料，包含癌症病人存活狀況、多重癌症史、居住地區及其他分析所需之個人資料。本文將自此資料庫擷取美國愛荷華州之癌症病人資料為例作實證分析，並以貝氏統計分析中常用之模型比較標準如條件預測指標(CPO)、平均對數擬邊際概似函數值(ALMPL)、離差訊息準則(DIC)分別測試其可靠度。 | zh_TW |
| dc.description.abstract (摘要) | The databases of Geographic Information System (GIS) have gained attention among different fields of statisticians to develop and analyze models which account for spatial clustering and variation. There is an emerging interest in modeling spatially correlated survival data in public health and epidemiologic studies. In this article, we develop Bayesian multivariate semiparametric hierarchical models to incorporate both spatially correlated and uncorrelated frailties to answer the question of spatial variation in the survival patterns, and we use multivariate conditionally autoregressive (MCAR) model to detect that whether there exists the spatial cluster across different areas. The baseline hazard function will be modeled semiparametrically using mixtures of finite Polya trees. The SEER (Surveillance Epidemiology and End Results) database from the National Cancer Institute (NCI) provides comprehensive cancer data about patient’s survival time, regional information, and others demographic information. We implement our Bayesian hierarchical spatial models on Iowa cancer data extracted from SEER database. We illustrate how to compute the conditional predictive ordinate (CPO), the average log-marginal pseudo-likelihood (ALMPL), and deviance information criterion (DIC), which are Bayesian criterions for model checking and comparison among competing models. | en_US |
| dc.description.tableofcontents | 1. Introduction 1 2 Semiparametric Spatial Models for Multiple Cancers 4 2.1 Semiparametric proportional odds frailty models 4 2.2 CAR and MCAR 5 2.2.1. Univariate CAR models 5 2.2.2. Multivariate CAR models 8 2.3. Multivariate semiparametric proportional odds models for multiple cancers 10 2.4. Mixture of Polya trees priors for the baseline survival function 11 3. Numerical implementation 16 3.1 SEER database and multiple response 16 3.2 Models Implementation 18 3.3 The Mixture of Polya trees prior implementation 19 3.4 MCMC steps 20 4. Illustration 21 5. Summary and Future Work 34 References 36 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0095354013 | en_US |
| dc.subject (關鍵詞) | 空間聚集 | zh_TW |
| dc.subject (關鍵詞) | 比例勝算模型 | zh_TW |
| dc.subject (關鍵詞) | 貝氏階層模型 | zh_TW |
| dc.subject (關鍵詞) | 混合Polya樹 | zh_TW |
| dc.subject (關鍵詞) | 馬可夫鏈蒙地卡羅模擬 | zh_TW |
| dc.subject (關鍵詞) | 多變量條件自回歸模型 | zh_TW |
| dc.subject (關鍵詞) | 條件預測指標 | zh_TW |
| dc.subject (關鍵詞) | 平均對數擬邊際概似函數值 | zh_TW |
| dc.subject (關鍵詞) | 離差訊息準則 | zh_TW |
| dc.subject (關鍵詞) | spatial clusters | en_US |
| dc.subject (關鍵詞) | proportional odds | en_US |
| dc.subject (關鍵詞) | Bayesian hierarchical model | en_US |
| dc.subject (關鍵詞) | mixture of Polya trees | en_US |
| dc.subject (關鍵詞) | Markov Chain Monte Carlo (MCMC) | en_US |
| dc.subject (關鍵詞) | multivariate conditionally autoregressive (MCAR) | en_US |
| dc.subject (關鍵詞) | average log-marginal pseudo-likelihood (ALMPL) | en_US |
| dc.subject (關鍵詞) | conditional predictive ordinate (CPO) | en_US |
| dc.subject (關鍵詞) | deviance information criterion (DIC) | en_US |
| dc.title (題名) | 空間相關存活資料之貝氏半參數比例勝算模式 | zh_TW |
| dc.title (題名) | Bayesian semiparametric proportional odds models for spatially correlated survival data | en_US |
| dc.type (資料類型) | thesis | en |
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