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- W2601454941 abstract "Longitudinal data are sometimes collected with alarge number of potential exploratory variables. In order to getthe better statistical inference and make the more accurateprediction, model selection has become an important procedure forlongitudinal studies. Nevertheless, the inference based on asingle model may ignore the uncertainty introduced by theselection procedure, and therefore underestimate the variability.As an alternative, model averaging approach combines estimates fromdifferent candidate models in the form of the certain weightedmean to reduce the effect of selection instability. There has beenmuch literature about model selection and averaging forcross-sectional data, but more efforts are needed to invest inlongitudinal data. My thesis focuses on model selection and modelaveraging procedures in the longitudinal data context. We proposean AIC-type model selection criterion (AIC) incorporating thegeneralized estimating equations approach. Specifically, weconsider the difference between the quasi-likelihood of acandidate model and a narrow model plus a penalty term in order toavoid the complicated integration calculation from thequasi-likelihood. This criterion actually inherits theoreticalasymptotic properties from AIC. In the second part, we develop afocused information criterion (QFIC) and a Frequentist modelaverage (QFMA) procedure on the basis of a quasi-score functionincorporating the generalized estimating equations approach. Thesemethods are shown to have asymptotic properties. We also conductintensive simulation studies to examine the numerical performanceof the proposed methods. The third part aims to apply the focusedinformation criterion to personalized medicine. Based on theindividual level information from clinical observations,demographics, and genetics, this criterion provides a personalizedpredictive model to make a prognosis and diagnosis for anindividual subject. Consideration of the heterogeneity ofindividuals helps to reduce prediction uncertainty and improveprediction accuracy. Several real case studies from biomedicalresearch are studied as illustrations." @default.
- W2601454941 created "2017-04-07" @default.
- W2601454941 creator A5067965873 @default.
- W2601454941 date "2014-01-01" @default.
- W2601454941 modified "2023-09-26" @default.
- W2601454941 title "Model selection and model averaging for longitudinal data with application in personalized medicine" @default.
- W2601454941 hasPublicationYear "2014" @default.
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