Nonlinear Quantile Regression Estimation of Longitudinal Data

This paper examines a weighted version of the quantile regression estimator defined by Koenker and Bassett (1978), adjusted to the case of nonlinear longitudinal data. Different weights are used and compared in a simulation study, using a four-parameter logistic growth function and error terms following an AR(1) model. It is found that the nonlinear quantile regression estimator is performing well, especially for the median regression case, that the differences between the weights are small, and that the estimator performs better when the correlation in the AR(1) model increases. A comparison is also made with the corresponding mean regression estimator, which is found to be less robust. Finally the estimator is applied to a data set with growth patterns of two genotypes of soybean, which gives some insights into how the quantile regressions give a more complete picture of the data than the mean regression does. Key words: dependent errors, median regression, panel data, repeated meas...

• Type: Journal
• Year: 2008
• Where: CSSC
• Authors: Andreas Karlsson
• Keywords: CSSC 2008, Median Regression, Nonlinear Quantile Regression, Quantile Regression Estimator