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- W2802397860 abstract "Abstract The observed data are often from subjects measured repeatedly over time. This kind of data with repeated responses is known as longitudinal data. Such data are from the subjects measured repeatedly over time and correlation may arise between measurements from the same topical subject. One of the advantages of the longitudinal data is that one can be able to specify the individual patterns of change for each subject. In this thesis, we develop a more general linear quantile regression model by extending the generalized quasi-likelihood method under mean regression to the quantile regression. Because the usage of the general stationary auto-correlation matrix, we do not need to specify any particular working correlation structure between repeated measurements. We can prove that parameter estimators obtained by our proposed method are consistent and asymptotically normal. By applying the induced smoothing method, we can introduce several methods to estimate quantile regression models built on longitudinal datasets." @default.
- W2802397860 created "2018-05-17" @default.
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- W2802397860 date "2018-01-01" @default.
- W2802397860 modified "2023-09-26" @default.
- W2802397860 title "The Proposed Quantile Regression Model For Longitudinal Data" @default.
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- W2802397860 doi "https://doi.org/10.1016/j.procs.2018.04.267" @default.
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