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- W216928692 abstract "Quantile regression is a powerful statistical methodology that complements the classicallinear regression by examining how covariates influence the location, scale, andshape of the entire response distribution and offering a global view of the statisticallandscape. In this thesis we propose a new quantile regression model for longitudinaldata. The proposed approach incorporates the correlation structure betweenrepeated measures to enhance the efficiency of the inference. In order to use theNewton-Raphson iteration method to obtain convergent estimates, the estimatingfunctions are redefined as smoothed functions which are differentiable with respectto regression parameters. Our proposed method for quantile regression provides consistentestimates with asymptotically normal distributions. Simulation studies arecarried out to evaluate the performance of the proposed method. As an illustration,the proposed method was applied to a real-life data that contains self-reported laborpain for women in two groups." @default.
- W216928692 created "2016-06-24" @default.
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- W216928692 date "2014-05-01" @default.
- W216928692 modified "2023-09-28" @default.
- W216928692 title "Quantile regression for longitudinal data" @default.
- W216928692 hasPublicationYear "2014" @default.
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