臺灣博碩士論文加值系統

時間序列模型的選取一直是時間序列分析的一個重要關鍵，而在本文當中闡述了Le, N.D., Martin, R.D. and Raftery, A.E. (1996)所提出的高斯混合轉換分佈(GMTD)模型，發現其模型利用了將數個常態分配混合的動作，使得模型更具有彈性，並在本文中藉由電腦模擬的結果比較GMTD模型以及ARIMA模型對於時間序列資料的配適結果；另外，提出一貝氏估計方法，採用Gibbs sampler以求得模型的參數估計值，也希望藉此改善EM演算法可能碰到的問題。

第一章、緒論……………………………………………………………3
第二章、模型描述與選取………………………………………………5
第一節、模型介紹…………………………………………………5
第二節、模型性質…………………………………………………6
第三節、模型選取…………………………………………………8
第三章、分析方法－EM演算法…………………………………………9
第四章、資料分析………………………………………………………16
第五章、貝氏估計方法…………………………………………………19
第一節、模型概要…………………………………………………19
第二節、Gibbs sampler…………………………………………26
第六章、結論……………………………………………………………29
附錄……………………………………………………………………．30
參考文獻………………………………………………………………．32

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