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- W833255706 abstract "SELECTING THE BEST LINEAR MIXED MODEL USING PREDICTIVE APPROACHES Jun Wang Department of Statistics Master of Science The linear mixed model is widely implemented in the analysis of longitudinal data. Inference techniques and information criteria are available and well-studied for goodnessof-fit within the linear mixed model setting. Predictive approaches such as 2 adj R , PRESS , and adj CCC are available for the linear mixed model but require more research (Edward, 2005). This project used simulation to investigate the performance of 2 adj R , PRESS, adj CCC , Pseudo F-test and information criterion for goodness-of-fit within the linear mixed model framework. Marginal and conditional approaches for these predictive statistics were studied under different variance-covariance structures. For compound symmetry structure, the success rates for all 17 statistics (marginal and conditional PRESS, CCC, and 2 R , F test, AIC and BIC) were high. The study suggested using marginal rather than conditional residuals for PRESS, CCC and 2 R . It suggested using REML likelihood function which has the term | | log 2 1" @default.
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- W833255706 modified "2023-09-27" @default.
- W833255706 title "Selecting the Best Linear Mixed Model Using Predictive Approaches" @default.
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