完整後設資料紀錄
DC 欄位語言
dc.contributor.author顏妤樺en_US
dc.contributor.authorYen, Yu-Huaen_US
dc.contributor.author洪慧念en_US
dc.contributor.authorHung, Hui-Nienen_US
dc.date.accessioned2014-12-12T02:40:47Z-
dc.date.available2014-12-12T02:40:47Z-
dc.date.issued2013en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT070152608en_US
dc.identifier.urihttp://hdl.handle.net/11536/74528-
dc.description.abstract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。zh_TW
dc.description.abstractSince 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.en_US
dc.language.isoen_USen_US
dc.subject高維度模型zh_TW
dc.subject模型選取zh_TW
dc.subjectAICzh_TW
dc.subjectBICzh_TW
dc.subjectEBICzh_TW
dc.subjectHigh Dimensional Modelen_US
dc.subjectModel Selectionen_US
dc.subjectAICen_US
dc.subjectBICen_US
dc.subjectEBICen_US
dc.titleAIC、BIC和EBIC之回顧zh_TW
dc.titleReview of AIC, BIC and EBICen_US
dc.typeThesisen_US
dc.contributor.department統計學研究所zh_TW
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