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Quantile regression in longitudinal studies with dropouts and measurement errors

机译:纵向研究中的分位数回归,具有辍学和测量误差

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Quantile regression models, as an important tool in practice, can describe effects of risk factors on the entire conditional distribution of the response variable with its estimates robust to outliers. However, there is few discussion on quantile regression for longitudinal data with both missing responses and measurement errors, which are commonly seen in practice. We develop a weighted and bias-corrected quantile loss function for the quantile regression with longitudinal data, which allows both missingness and measurement errors. Additionally, we establish the asymptotic properties of the proposed estimator. Simulation studies demonstrate the expected performance in correcting the bias resulted from missingness and measurement errors. Finally, we investigate the Lifestyle Education for Activity and Nutrition study and confirm the effective of intervention in producing weight loss after nine month at the high quantile.
机译:作为实践中的重要工具,分位数回归模型可以描述风险因素对响应变量的整个条件分布的影响,其估计值对异常值具有鲁棒性。但是,很少有关于纵向数据的分位数回归的讨论,因为缺少响应和测量误差,这在实践中很常见。我们针对具有纵向数据的分位数回归开发了加权校正和偏倚校正的分位数损失函数,它既允许缺失也可以进行测量误差。此外,我们建立了所提出估计量的渐近性质。仿真研究表明,在纠正由缺失和测量误差引起的偏差方面的预期性能。最后,我们调查了运动和营养生活方式教育研究,并确认了在高分位数的9个月后干预对减轻体重的效果。

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