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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/30942


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    题名: 廣義線性混合模式結合B-Spline在疾病地圖上之應用
    Applying GLMM with B-Spline to Map Disease Rates
    作者: 連家斌
    贡献者: 陳麗霞
    連家斌
    关键词: GLMM
    B-spline
    CAR
    日期: 2004
    上传时间: 2009-09-14
    摘要: 本論文探討了以廣義線性混合模式(GLMM)結合時間及空間效果的時間空間模式,以將地區特性、人口特徵等變數,及時間變數納入模式中。有關時間效果可用B-Spline方法建構固定或隨機的時間趨勢平滑函數,而空間效果則是將各地區的隨機效果以條件自我相關模式(CAR)描述。實證部份則是應用GLMM模式分析台灣本島350個鄉鎮市區自民國八十八年到九十一年的肝癌就診資料,依性別、年齡層加以整理,並將年齡層分為0~19歲、20~39歲、40 ~59歲、60歲以上,分別代表少、青、壯、老等四個年齡層;再採用GLMMGibbs結合R軟體各個資料集分別配適時間空間模式,估計各地區之相對風險並繪製疾病地圖,據以找出各年估計的相對風險高的地區。
    參考文獻: 中文部份:
    1.賴景義:電腦輔助設計-B-spline基本特性介紹。
    http://www.me.ncu.edu.tw/jylai/CAD/B-spline.doc
    2.行政院衛生署--衛生統計資訊網。
    http://www.doh.gov.tw/statistic/index.htm
    3.財團法人預防醫學基金會-認識肝癌。
    http://www.pmf.org.tw/hcc.htm
    4. 蕭朱杏、莊愷瑋 (2001).地理統計於醫學與環境的應用,地理統計在農業和環境科學之應用研討會論文集,79-92頁,中國農業化學會。
    5. 陳定信、賴明陽、陳健弘 (1991).本土醫學資料庫之建立及衛生政策上之應用,行政院衛生署八十年度委託研究計畫研究報告。
    6. 行政院衛生署國民健康局 (2003).中華民國癌症死亡率分佈地圖集(1972-2001)。
    7. 行政院衛生署國民健康局 (2003).中華民國癌症發生率分佈地圖集(1995-1998)。
    英文部份:
    1.Bernardinelli, L. and Montomoli, C. (1992). Empirical Bayes versus fully Bayesian analysis of geographical variation in disease risk. Statistics in Medicine. 11, 983-1007.
    2.Besag, J., York, J. and Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics. 43, 1-21.
    3.Breslow, N. E. and Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association. 88, 421, 9-25.
    4.Chambers, J. M. and Hastie, T. J. (1992). Chapter 7 of Statistical Models in S. Pacific Grove, Calif.:Wadsworth & Brooks/Cole Advanced Books & Software.
    5.Clayton, D. and Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics , 43, 671-681.
    6.De Boor, C. (1978). A Practical Guide to Splines. New York:Springer-Verlag.
    7.Gelfand, A. E. and Smith, A.F.M. (1990). Sampling based approaches to calculating marginal desities. J. Am. Statist. Ass. 85, 389-409.
    8.Gelfand, A. E., Hills, S. E., Racine-Poon, A. and Smith, A. F. M. (1990). Illustration of Bayesian inference in normal data models using Gibbs sampling. J. Am. Statist. Ass. 85, 972-985.
    9.Lawson, A. B., Browne, W. J. and Vidal Roderiro, C. L. (2003). Disease Mapping with Winbugs and MLwin. England:John Wiley.
    10.Myles, J. and Clayton, D. (2001). GLMMGibbs:An R package for estimating Bayesian Generalised Linear Mixed Models by Gibbs Sampling.
    11.MacNab, Y. C. and Dean, C. B.(2000). Parametric bootstrap and penalized quasi-likelihood inference in conditional autoregressive models. Statistics in Medicine, 19, 2421-2435.
    12.MacNab, Y. C. and Dean, C. B. (2001). Autoregressive spatial smoothing and temporal spline smoothing for mapping rates. Biometrics, 57, 949-956.
    13.MacNab, Y. C. and Dean, C. B. (2002). Spatio-temporal modelling of rates for the construction of disease maps. Statistics in Medicine, 21, 347-358.
    14.McCulloch, C. E. and Searle, S. R. (2001). Generalized, Liner, and Mixed models. New York:John Wiley.
    15.Pickle, L. W. (2000). Exploring spatio-temporal patterns of mortality using mixed effects models. Statistics in Medicine, 19, 2251-2263.
    16.Shene, C. K. (2003). Introduction to Computing with Geometry Notes.
    http://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/notes.html
    17.Tsutakawa, R. K. (1988). Mixed model for analyzing geographical variability in mortality rates. J. Am. Statist. Ass., 83, 37-42.
    18.Venables, W. N. and Ripley, B.D. (2002). Modern Applied Statistics with S. New York:Springer.
    19.Walter, S. D. and Birnie, S. E. (1991). Mapping mortality and morbidity patterns: an international comparison. International Journal of Epidemiology .20 (3), 678-689.
    描述: 碩士
    國立政治大學
    統計研究所
    92354010
    93
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0923540101
    数据类型: thesis
    显示于类别:[統計學系] 學位論文

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