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研究生: 張逸塵
Yi-chen Zhang
論文名稱: 混合先驗分佈下誤差項為自我迴歸之線性混合效應模型的貝氏分析
Bayesian Inference on the Linear Mixed-Effect Models with Autoregressive Errors Using Mixture Priors
指導教授: 樊采虹
Tasi-hung Fan
于振華
Jenn-hwa Yu
口試委員:
學位類別: 碩士
Master
系所名稱: 理學院 - 統計研究所
Graduate Institute of Statistics
畢業學年度: 97
語文別: 中文
論文頁數: 56
中文關鍵詞: 自我迴歸模型吉比氏抽樣法混合先驗分佈隨機效應固定效應貝氏預測中位機率模型
外文關鍵詞: Fixed effect, Random effect, Mixture prior, Gibbs sampling, AR(1) model, Median probability model, Bayesian prediction
相關次數: 點閱:19下載:0
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  • 本文主要目的在於探討誤差項具自我迴歸之貝氏線性混合效應模型。經由混合先驗分佈,找出顯著的固定效應與隨機效應變數,進而得到中位機率模型,並由該模型進行貝氏之推論與預測。最後將此模型應用在一組高血糖和相對高胰島素血症,對於葡萄糖的容忍度實驗之資料中,結果顯示,本文提出的方法可以提供準確的預測。


    In this thesis, we address the problem of Bayesian linear mixed effects model with auto-regressive errors. We consider a mixture prior to identify subsets of covariates having nonzero fixed effect coefficients or nonzero random effects variance, and eventually obtain a median probability model, which is utilized for Bayesian inference and prediction. Finally, the proposed method is applied to a study of the association of hyperglycemia and relative hyperinsulinemia, and it yields very accurate prediction results.

    中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . i 英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii 誌謝辭. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv 圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v 表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi 第一章緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 研究動機與背景. . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 文獻探討與方法回顧. . . . . . . . . . . . . . . . . . . . . . 4 1.3 研究方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 第二章混合先驗分佈下之貝氏線性混合效應模型. . . . . 7 2.1 貝氏線性迴歸模型. . . . . . . . . . . . . . . . . . . . . . . 8 2.2 貝氏線性混合效應模型. . . . . . . . . . . . . . . . . . . . . 12 2.3 中位機率模型. . . . . . . . . . . . . . . . . . . . . . . . . . 16 第三章誤差具自我迴歸的貝氏線性混合效應模型. . . . . 19 3.1 貝氏推論. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.1 相關係數相同之模型. . . . . . . . . . . . . . . . . . 19 3.1.2 相關係數不同之模型. . . . . . . . . . . . . . . . . . 23 3.2 線性混合效應之中位機率模型. . . . . . . . . . . . . . . . . 25 3.3 貝氏預測. . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 第四章模擬分析與實例應用. . . . . . . . . . . . . . . . . . 29 4.1 線性混合效應模型之模擬分析. . . . . . . . . . . . . . . . . 29 4.2 誤差具自我迴歸的線性混合效應模型之模擬分析. . . . . . . . 32 4.3 實例分析與預測. . . . . . . . . . . . . . . . . . . . . . . . 36 第五章結論. . . . . . . . . . . . . . . . . . . . . . . . . . . 42 參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

    [1] Akaike, H. (1974). A New Look at the Statistical Model Identification.
    IEEE Transactions on Automatic Control 19, 716-723.
    [2] Barbieri, M. M. and Berger, J. O. (2004). Optimal Predictive Model
    Selection. The Annals of Statistics 32, 870-897.
    [3] Barnard, G. A. (1963). New Methods of Quality Control. Journal of
    the Royal Statistical Society: Series A 126, 255-258.
    [4] Burnham, K. P. and Anderson, D. R. (2004). Multimodel Inference:
    Understanding AIC and BIC in Model Selection. Sociological Methods
    and Research 33, 261-304.
    [5] Cai, B. and Dunson, D. B. (2006). Bayesian Covariance Selection in
    Generalized Linear Mixed Models. Biometrics 62, 446-457.
    [6] Carlin, B. P. and Louis, T. A. (2008). Bayes and Empirical Bayes
    Methods for Data Analysis. Chapman and Hall, London. 3rd ed.
    [7] Chen, Z. and Dunson, D. B. (2003). Random Effects Selection in Linear
    Mixed Models. Biometrics 59, 762-769.
    [8] Chi, E. M. and Reinsel, G. C. (1989). Models for Longitudinal Data
    with Random Effects and AR(1) Errors. Journal of the American Sta-
    tistical Association 84, 452-459.
    [9] Chib, S. (1993). Bayes Regression with Autoregressive Errors: A
    Gibbs Sampling Approach. Journal of Econometrics 58, 275-294.
    [10] Clyde, M. A. (1999). Bayesian Model Averaging and Model Search
    Strategies. In Bayesian Statistics 6 (Bernardo, J. M., Berger, J. O.
    Dawid, A. P. and Smith, A. F. M. eds.) 157-185. Oxford University
    Press.
    [11] Collins, L. M. and Horn, J. L. (eds.) (1991). Best Methods for the
    Analysis of Change. Washington, DC: America Psychological Association.
    [12] Cox, D. R. and Oakes, D. (1984). Analysis of Survival Data. Chapman
    and Hall, London.
    [13] Crainiceanu, C. and Ruppert, D. (2004). Likelihood Ratio Tests in
    Linear Mixed Models with One Variance Component. Journal of the
    Royal Statistical Society B 66, 165-185.
    [14] Dale, A. and Davies, R. B. (eds.) (1994). Analyzing Social and Political
    Change: A Casebook of Methods. London: Sage.
    [15] Diggle, P. J. (1990). Time Series: A Biostatistical Introduction. Oxford
    University Press, Oxford.
    [16] Dutilleul, P. and Pinel-Alloul, B. (1996). A doubly Multivariate Model
    for Statistical Analysis of Spatio-Temporal Environmental Data. En-
    vironmetrics 7, 551-565.
    [17] Fan, T. H., Wang, G. Z., and Yu, J. H. (2009). A New Algorithm
    in Bayesian Model Averaging in Regression Models. Technical Report,
    National Central University.
    [18] Gelfand, A. E. and Smith A. F. M. (1990). Sampling-Based Approaches
    to Calculating Marginal Densities. Journal of the American
    Statistical Association 85, 398-409.
    [19] Geman, S. and Geman, D. (1984). Stochastic Relaxation, Gibbs Distribution
    and the Bayesian Restoration of Image. IEEE Transactions
    on Pattern Analysis and Machine Intelligence 6, 721-741.
    [20] George, E. I. and McCulloch, R. E. (1993). Variable Selection Via
    Gibbs Sampling. Journal of the American Statistical Association 88,
    881-889.
    [21] Geweke, J. (1996). Variable Selection and Model Comparison in Regression.
    In Bayesian Statistics 5|Proceedings of the Fifth Valencia
    International Meeting, Bernardo, J. M., Berger, J. O., Dawid, A. P.,
    and Smith, A. F. M. (eds), 609-620. Oxford, U.K.: Oxford University
    Press.
    [22] Gilks, W. R., Wang, C. C., Yvonnet, B., and Coursaget, P. (1993).
    Random-Effects Models for Longitudinal Data Using Gibbs Sampling.
    Biometrics 49, 441-453.
    [23] Gottman, J. M. (ed.) (1995). The Analysis of Change. Mahwah, N. J.:
    Lawrence Erlbaum.
    [24] Heitjan, D. F. and Sharma, D. (1997). Modelling Repeated-Series Longitudinal
    Data. Statistics in Medicine 16, 347-355.
    [25] Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T.
    (1999). Bayesian Model Averaging: A Tutorial (with discussion). Sta-
    tistical Science 14, 382-417.
    [26] Jennrich, R. I. and Schluchter, M. D. (1986). Unbalanced Repeated-
    Measures Models with Structured Covariance Matrices. Biometrics
    42, 805-820.
    [27] Kinney S. K. and Dunson, D. B. (2007). Fixed and Random Effects
    Selection in Linear and Logistic Models. Biometrics 63, 690-698.
    [28] Konishi, S. and Kitagawa, G. (2008). Information Criteria and Sta-
    tistical Modeling. New York: Springer-Verlag.
    [29] Kuo, L. and Mallick, B. (1998). Variable Selection for Regression Models.
    Sankhy¹a: The Indian Journal of Statistics, Series B 60, 65-81.
    [30] Laird, N. M. and Ware, J. H. (1982). Random-Effects Models for
    Longitudinal Data. Biometrics 38, 963-974.
    [31] Lin, X. (1997). Variance Component Testing in Generalized Linear
    Models with Random Effects. Biometrika 84, 309-326.
    [32] Lindstrom, M. J. and Bates, D. M. (1988). Newton-Raphson and EM
    Algorithms for Linear Mixed-Effects Models for Repeated Measures
    Data. Journal of the American Statistical Association 83, 1014-1022.
    [33] Leamer, E. E. (1978). Speci¯cation Searches. Wiley, New York.
    [34] McCulloch, C. E., Searle, S. R., and Neuhaus, J. M. (2008). General-
    ized, Linear, and Mixed Models. Wiley, 2nd ed.
    [35] Montgomery, D. C., Peck, E. A., and Vining, G. G. (2006). Introduc-
    tion to Linear Regression Analysis. Wiley, New York, 4th ed.
    [36] Raftery, A. E., Madigan, D., and Hoeting, J. A. (1997). Bayesian
    Model Averaging for Linear Regression Models. Journal of the Amer-
    ican Statistical Association 92, 179-191.
    [37] Robert, C. P. (2007). The Bayesian Choice. New York: Springer-
    Verlag. 2ed.
    [38] Roberts, H. V. (1965). Probabilistic Prediction. Journal of the Amer-
    ican Statistical Association 60, 50-62.
    [39] Schwarz, G. (1978). Estimating the Dimension of a Model. The Annals
    of Statistics 6, 461-464.
    [40] Stram, D. O. and Lee, J. W. (1994). Variance Components Testing in
    the Longitudinal Mixed Effects Model. Biometrics 50, 1171-1177.
    [41] Tsay, R. S. (2005). Analysis of Financial Time Series. Wiley, Hoboken,
    N. J., 2nd ed.
    [42] Zerbe, G. O. (1979). Randomization Analysis of the Completely Randomized
    Design Extended to Growth and Response Curves. Journal
    of the American Statistical Assocation 74, 215-221.
    [43] Zerger, S. L. and Karim, M. R. (1991). Generalized Linear Model
    with Random Effects: A Gibbs Sampling Approach. Journal of the
    American Statistical Association 86, 79-86.
    [44] 王國肇(2007)。貝氏模型平均演算法及其在長時期追蹤資料之應用。國
    立中央大學統計研究所碩士論文。

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