Quantile regression for longitudinal data

Lu, Xiaoming (2014) Quantile regression for longitudinal data. Masters thesis, Memorial University of Newfoundland.

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    Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
    (Original Version)

Abstract

Quantile regression is a powerful statistical methodology that complements the classical linear regression by examining how covariates influence the location, scale, and shape of the entire response distribution and offering a global view of the statistical landscape. In this thesis we propose a new quantile regression model for longitudinal data. The proposed approach incorporates the correlation structure between repeated measures to enhance the efficiency of the inference. In order to use the Newton-Raphson iteration method to obtain convergent estimates, the estimating functions are redefined as smoothed functions which are differentiable with respect to regression parameters. Our proposed method for quantile regression provides consistent estimates with asymptotically normal distributions. Simulation studies are carried 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 labor pain for women in two groups.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/6335
Item ID: 6335
Additional Information: Includes bibliographical references (pages 64-68).
Department(s): Science, Faculty of > Mathematics and Statistics
Date: May 2014
Date Type: Submission
Library of Congress Subject Heading: Quantile regression; Numerical analysis; Longitudinal method; Approximation theory; Analysis of covariance

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