The model selection under the cluster longitudinal data

• Main Authors: TSENG,CHIH-PIN, 曾智彬
• Other Authors: SHEN,CHUNG-WEI
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/84jx7e

碩士 === 國立中正大學 === 數學系統計科學研究所 === 107 === In our life, we routinely conduct health examination every year, or we read financial statements of companies at each quarter when investing. Another one, when working in production, we pay attention to the quality of production of every batch of each series, etc.. All of these are belong to clustered longitudinal data. And this data type is independent between the cluster and the cluster, and there is a vertical correlation between the factors considered in the cluster. The factors also have the characteristics of repeated observations, and the repeated observations are due to the passage of time, so they are also relevant. As early as 1992, the generalized estimating equation (GEE) proposed by Liang, Zeger and Qaqishy, and are applied to data analysis of this type. Later ,they discov- ered this data type that is easy to have missing values, which makes it difficult to accurately estimate parameters of models. Robins, Rtnitzky and Zhao’s published weighted generalized estimating equation (WGEE) in 1995, it can be used to analysis the data type with missing values. In this paper, we will apply weighted generalized estimating equations (WGEE) by ”Taylor’s expansion”, ”Perturbation method” and ”Bootstrap Method” to esti- mate the parameters when the data has missing values, and compare the advantages and disadvantages of the model selection. Finally, we use the data of routinely health examination by old human as the verification of the method above. Keywords：cluster; longitudinal; MLIC; WGEE