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政大典藏 > College of Commerce > Department of Statistics > Periodical Articles > Item 140.119/18111

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/18111

Title:	非高斯時間序列選模法之比較
Other Titles:	On Comparison of Model Selection Criteria for Non-Gaussian Time Series Models
Authors:	鄭天澤;林秀紅 Jeng,Tian-Tzer;Lin, Shiou-Horng ARMA模型;AIC
Date:	1993-10
Issue Date:	2008-12-19 14:47:59 (UTC+8)
Abstract:	近二十年來，時間序列分析中有關ARMA模型選取的準則中，較為人所接受且廣泛被應用的主要有AIC及BIC等。這些選模法在選模過程中均需給定候選模型的最高階次，此最高階次的給定不僅將影響最終模型的確認，且當其值很大時，整個選模過程會變得相當複雜且煩瑣。Pukkila et al.(1990)以對稱性增加配適模型階次的疊代過程所提出之選模方法(簡記為PKK法)，則不需給定候選模型最高階次。但是上述方法皆基於更新序列(innovationseries)為高斯過程的假設，在實例中，此等假設往往不成立。本文主要目的在探討當更新序列不為高斯過程時，PKK選模法的選模能力；並將其結果與由AIC及BIC選模法在非高斯過程下之選模結果做比較。
Relation:	國立政治大學學報, 67(下), 473-491
Data Type:	article
Appears in Collections:	[Department of Statistics] Periodical Articles [Issue 67] Journal Articles

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