群集資料/長期追蹤資料下重覆抽樣之模型選擇方法;Resampling Model Selection for Cluster/Longitudinal Data

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Title:	群集資料/長期追蹤資料下重覆抽樣之模型選擇方法;Resampling Model Selection for Cluster/Longitudinal Data
Authors:	王博賢;WANG, BO-SIAN
Contributors:	數學系統計科學研究所
Keywords:	長期追蹤資料;模型選擇;重複抽樣;群集大小有資訊;Longitudinal;Model Selection;Resampling;Cluster Size is Informative
Date:	2017
Issue Date:	2019-07-17
Publisher:	數學系統計科學研究所
Abstract:	在分析長期追蹤資料與群集資料時，常使用的方法之一為Liang and Zeger(1986) 所提出的廣義估計方程式。在群集資料中，群集大小不同是很常見的情況，因此群集大小是否有資訊就變成研究者感興趣的一個問題。而因為在群集大小有資訊時，廣義估計方程式會有偏差。所以Hoffman et al.(2001) 提出群內重複抽樣解決這個問題。但需要大量時間運算，因此 Williamson et al. 提出群加權廣義估計方程式，並證明群加權廣義估計方程式與群內重複抽樣是一致的，且需要的時間更短。選模指標一直以來是個重要的議題。在群集大小沒有資訊時，常用的有Shen and Chen(2012) 提出的 MLIC (missing longitudinal information criterion)和 Pan(2001) 所提出的 QIC，然而在群集大小有資訊時，並沒有一個好的選模指標。因此在本文中，我們將利用群內重複抽樣，與誤差平方和的概念來建構新的選模指標 Resampling Longitudinal lnformation Criterion (RLIC)。並且利用模擬研究來佐證RLIC不管在群集大小有沒有資訊都能有不錯的結果，最後利用新的選模指標RLIC對實際資料作分析。 The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the Cluster weighted GEE approach (CWGEE, Williamson et al., 2003), can further accommodate data when cluster size is informative. The model selection issue in the GEE framework has been discussed in previous literature when cluster size is non-informative. However, it is still unknown how to perform model selection in GEE analysis when the cluster size is informative. In this work, we have proposed a resampling model selection criterion RLIC for selection of the mean model in the GEE model. The proposed method is shown to perform well when cluster size is non-informative/informative. We also examine the performance of a naive procedure namely to apply the QIC method (Pan, 2001) with informative cluster size treated as non-informative. We find that such a naive application of QIC do not perform as well as the RLIC method. The proposed method is further applied to data from the Taiwan longitudinal study on aging to assess the marginal relationship of frailty status with health and social status in the elderly, when cluster size is informative.
Appears in Collections:	[數學系統計科學研究所] 學位論文

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