長期追蹤資料下模型選擇方法之比較

• Main Authors: Wei-Te Chiang, 江韋德
• Other Authors: Chung Wei Shen
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/823brq

碩士 === 國立中正大學 === 數學系統計科學研究所 === 102 === Recent developments in the field of Statistics have led to an interest in model selection. Mallows's CP (Mallows, 1973), AIC (Akaike, 1974) , BIC (Schwarz, 1978) are common methods applied to select models when data is independent. Longitudinal data is a common type of data in many fields; however, it is not proper to use above methods for model selection due to the repetitive observations. As a consequence, a recent study by Junchi and Liu (2014) has proposed model selection principles in misspecified models by GAIC and GBIC. Moreover, Shen and Chen (2012) proposed the missing longitudinal information criterion (MLIC) for GEE analysis when the outcome data are subject to dropout. This present study attempts to investigate the advantages and disadvantages of GAIC, GBIC, and MLIC model selection methods through the simulation studies. Besides, this present study also uses these model selection methods to analyze real data.