Ӧ�ø���ͳ�� 2014, 30(3) 296-302 DOI:      ISSN: 1001-4268 CN: 31-1256

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Quantile Regression for Growth Curve Model
Zhang Yu, Liu Qian, Zeng Linrui
College of Mathematics, Sichuan University; School of Finance and Statistics, East China Normal University

Growth curve model has broad application background,
and plays an important role in some fields such as economics, biology, medical
research. Many of existing estimation of its parameter matrix have been
obtained based on the least squares method or maximum likelihood method.
When distribution of the error term is partial peak, or heavy tail, or there
exist outliers, estimation obtained by least square method will be invalid.
The distribution of the error must be known in maximum likelihood estimation,
which is often not satisfied. Quantile regression method can compensate for
these defects and the estimation has good robustness. In this paper, quantile
regression is used to give the estimation of growth curve model, and its
asymptotic normality.

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