Bloomfield（1973）針對擁有短記憶（short-memory）性質之時間序列，提出指數型態之譜密度函數模型（稱為Bloomfield模型或EXP模型）。Beran（1993）進一步將Bloomfield模型推廣至長記憶（long-memory）模式下的fractional exponential模型（FEXP模型）。Robinson（1995）、Hurvich & Brodsky（1998）以及Moulines & Soulier（1999）針對FEXP模型，提出譜密度函數之參數估計量並推導出該估計量之大樣本性質。

　　本研究之目標即對於擁有長記憶性質的FEXP模型，利用最小平方法與LASSO兩種估計方法，比較其參數估計量表現之優劣以及不同選模準則對於建構迴歸模型之影響。

　　模擬結果顯示，短記憶模式結構的型態是影響長距相關參數估計量表現優劣的重要因素。當短記憶模式結構較為單純時，以最小平方法搭配Mallow’s criterion（Mallow, 1973）之估計量表現為最佳（相較於其他參數估計方法與選模準則之組合），然而當短記憶模式結構越複雜時，LASSO估計量之表現將優於最小平方估計量。但不論樣本大小及參數估計方法為何，當長距相關參數越接近參數空間之邊界時，兩種估計量之表現也隨之越不理想。

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