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研究生:何秀榮
研究生(外文):Hsiu J. Ho
論文名稱:具遺失訊息下多變量混合常態模型之快速監督學習
論文名稱(外文):On fast supervised learning for normal mixture models with missing information
指導教授:林宗儀林宗儀引用關係
指導教授(外文):Tsung I. Lin
學位類別:碩士
校院名稱:東海大學
系所名稱:統計學系
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:英文
論文頁數:27
外文關鍵詞:Bayesian classifierData augmentationEM algorithmIncomplete featuresRao-Blackwellization
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用遺失值的資料配適混合模型(mixtrue models)是一個重要的研究課題。在本文中,在假設隨機遺失訊息"missing at random"(MAR)情況下,我們引進輔助的指標矩陣來處理多變量混合常態模型。我們發展一個新結構的EM演算法可大幅地節省運算時間並且有許多應用,例如:密度估計、分類與遺失值的預測。對於遺失資料的多重設算(multiple impuation),我們利用吉氏抽樣法(Gibbs sampler)提出一個新的資料擴增(data augmentation)演算法。在考慮不同的人為遺失比例下,我們用一些實例來闡述所提出的方法。
It is an important research issue to deal with mixture models when missing values occur in the data. In this paper, computational strategies using auxiliary indicator matrices are introduced for handling mixtures of multivariate normal distributions in a more efficient manner, assuming that patterns of missingness are arbitrary and missing at random. We develop a novelly structured EM algorithm which can dramatically save computation time and be exploited in many applications, such as density estimation, supervised clustering and prediction of missing values. In the aspect of multiple imputations for missing data, we also offer a data augmentation scheme using the Gibbs sampler. Our proposed methodologies are illustrated through some real data sets with varying proportions of missing values.
1. Introduction
2. A normal mixture model with missing information
3. An efficient EM procedure for ML estimation
4. A data augmentation scheme for Bayesian sampling
5. Experimental results
6. Conclusions
Anderson, E. 1935. The irises of the Gasp\'{e} Peninsula, Bulletin of the American Iris Society. 59, 2-5.

Basford K.E., McLachlan G.J. 1985. Estimation of allocation rates in a cluster analysis text. J. Amer. Statist. Assoc. 80, 286-293.

Brooks S.P., Gelman A. 1998. General methods for monitoring convergence of iterative simulations, J. Comp. Graph. Statist. 7, 434-455.

Campbell, N.A., Mahon, R.J. 1974. A multivariate study of variation in two species of rock crab of genus Leptograpsus, Aust. J. Zoology 22, 417-425.

Celeux G., Hurn M., Robert C.P. 2000. Computational and inferential difficulties with mixture posterior distributions, J. Amer. Statist. Assoc. 95, 957-970.

Dempster A.P., Laird N.M., Rubin D.B. 1977. Maximum likelihood from incomplete data via the EM algorithm (with discussion), J. R. Stat. Soc. B. 39, 1-38.

Diebolt J., Robert C.P. 1994. Estimation of finite mixture distributions through Bayesian sampling, J. R. Stat. Soc. B. 56, 363-375.

Edwards W.H. Lindman, Savage L.J. 1963. Bayesian statistical inference for psychological research, Psycol. Rev. 70, 193-242.

Escobar M.D., West M. 1995. Bayesian density estimation and inference using mixtures, J. Amer. Statist. Assoc. 90, 577-88.

Fisher R.A. 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics. 7, Part II, 179-188.

Fruhwirth-Schnatter S. 2001. Markov Chain Monte Carlo estimation of classical and dynamic switching and mixture models, J. Amer. Statist. Assoc. 96, 194-209.

Gelfand A.E., Smith A.F.M., 1990. Sampling based approaches to calculate marginal densities, J. Amer. Statist. Assoc. 85, 398-409.

Geman S., Geman D., 1984. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. Intell. 6, 721-741.

Ghahramani Z., Jordan M.I., 1994. Supervised learning from incomplete data via an EM approach, In: Cowan, J.D., Tesarro, G., Alspector, J. (Eds), Advances in Neural Information Processing Systerms, vol. 6. Morgan Kaufmann Publishers, San Francisco, CA, pp. 120-127.

Green P.J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika. 82, 711-732 Johnson R.A., Wichern D.W. 2002. Applied Multivariate Statistical Analysis, 5th ed, Prentice Hall.

Lin T.I., Lee J.C., Ni H.F. 2004. Bayesian Analysis of Mixture Modelling using the Multivariate $t$ Distribution, Statist. Comput. 14, 119-130

Little, R.J.A., Rubin, D.B. 2002. Statistical analysis with missing data, 2nd ed. New York, Wiley.

Liu C.H. 1999. Efficient ML estimation of multivariate normal distribution from incomplete data. J. Multivariate. Anal. 69, 206-217.

McLachlan G.J., Basford K.E. 1988. Mixture Models: Inference and Application to Clustering, New York, Marcel Dekker.

McLachlan G.J., Peel D. 2000. Finite Mixture Model, New York, Wiely.

Peel D., McLachlan G.J. 2000. Robust mixture modeling using the $t$ distribution, Statist. Comput. 10, 339-348.

Reaven G.M., Miller R.G. 1979. An attempt to define the nature of chemical diabetes using a multidimensional analysis, Diabetologia. 16, 17-24.

Rubin, DB. 1976. Inference and missing data, Biometrika. 63, 581-592.

Richardson S., Green P.J. 1997. On Bayesian analysis of mixtures with an unknown number of components, J. R. Stat. Soc. B. 59, 731-792.

Schafer J.L. 1997. Analysis of Incomplete Multivariate Data, London, Chapman and Hall.

Shoham S. 2002. Robust clustering by deterministic agglomeration EM of mixtures of multivariate $t$-distributions, Pattern Recognition. 35, 1127-1142.

Stephens M. 2000. Bayesian analysis of mixture models with an unknown number of components -- an alternative to reversible jump methods, Ann. Statist. 28, 40-74.

Stone M. 1974. Cross-validatory choice and assessment of statistical prediction (with discussion). J. R. Stat. Soc. B. 36, 111-147.

Tanner M.A., Wong W. H. 1987. The calculation of posterior distributions by data augmentation (with discussion), J. Am. Statist. Assoc. 82, 528-550.

Titterington, D.M., Smith, A.F.M., Markov, U.E. 1985. Statistical Analysis of Finite Mixture Distributions, New York, Wiely.

Wang H.X., Zhang Q.B., Luo B., Wei S. 2004. Robust mixture modelling using multivariate $t$ distribution with missing information, Pattern Recognition Lett. 25, 701-710.

Zhang Z.H., Chan K.L., Wu Y.M., Chen C.B. 2004. Learning a multivariate gaussian mixture model with the reversible jump MCMC algorithm, Statist. Comput. 14, 343-355.
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