本論文探討時空隨機效應模式的選模問題，由於模型中的隨機效應過多，若直接利用選模準則 AIC / BIC 選擇模型，因所需的計算量太大，在分析真實資料時不易執行。為了簡化計算量，我們採用前進選取法，將隨機效應依解釋變異之重要性排序，建構出一選模路徑，在此選模路徑上再配合 AIC / BIC 進行隨機效應的模式選取。在參數估計上，採用 EM 演算法，能有效率的計算 MLE。模擬結果顯示所提出之前進選取配合 AIC 的方法有良好的預測表現。

關鍵字：時空隨機效應模型、模型選取、前進選取法

In this thesis, we discuss the selection problems for spatial temporal random effect models, due to excessive random effects model, if we directly use the model selection criteria AIC/BIC to selection models, because the amount of computation required is too large, in the analysis of real data is not easy to perform. To simplify the calculation, we use the concept of forward selection to sort the random effects by the importance of explaning the variance, then construct a selection path.In this path, use AIC/BIC to select the best model. In the parameter estimation, using EM algorithm can efficiently compute MLE. Simulation results show that the proposed method of forward selection with AIC has good predictive performance.

key words：spatial temporal random effect model、model selection、forward selection

第一章緒論 1
第二章動態時空統計模型 3
第三章參數估計 9
第四章模型排序及選取 14
第五章模擬分析 18
第六章結論 46
參考文獻47

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