標題: AIC、BIC和EBIC之回顧
Review of AIC, BIC and EBIC
作者: 顏妤樺
Yen, Yu-Hua
洪慧念
Hung, Hui-Nien
統計學研究所
關鍵字: 高維度模型;模型選取;AIC;BIC;EBIC;High Dimensional Model;Model Selection;AIC;BIC;EBIC
公開日期: 2013
摘要: 自資訊爆炸以來,利用統計方法分析資料漸漸成為一種常態。而我們所面對的問題也從過去的大樣本資料分析逐漸轉變成高維度資料分析。如何找出這些資料的最適模型是我們最重要的課題。在這篇文章中,我們將Chen & Chen (2008)提出之針對高維度模型選取方法EBIC與常見的模型選取方法AIC、BIC做比較,並利用模擬的方式說明這些方法的差異與優劣。
Since the information explosion, analyzing data by using statistical methods progressively becomes norm. Nowadays, the problem we are faced with large sample size analysis gradually transformed into high dimensional model analysis. How to find the optimal model for the data is our most important issue. In our study, we compare EBIC, which proposed by Chen & Chen (2008) for high dimensional model, with common model selection methods, AIC and BIC, and use simulations illustrating the difference and the pros and cons of these methods.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT070152608
http://hdl.handle.net/11536/74528
Appears in Collections:Thesis


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