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Rank regression analysis of correlated water quality data from South East Queensland

机译：东南昆士兰州相关水质数据的秩回归分析

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摘要

With growing population and fast urbanization in Australia, it is a challenging task to maintain our water quality. It is essential to develop an appropriate statistical methodology in analyzing water quality data in order to draw valid conclusions and hence provide useful advices in water management. This paper is to develop robust rank-based procedures for analyzing nonnormally distributed data collected over time at different sites. To take account of temporal correlations of the observations within sites, we consider the optimally combined estimating functions proposed by Wang and Zhu (Biometrika, 93:459–464, 2006) which leads to more efficient parameter estimation. Furthermore, we apply the induced smoothing method to reduce the computational burden. Smoothing leads to easy calculation of the parameter estimates and their variance-covariance matrix. Analysis of water quality data from Total Iron and Total Cyanophytes shows the differences between the traditional generalized linear mixed models and rank regression models. Our analysis also demonstrates the advantages of the rank regression models for analyzing nonnormal data.
机译：随着澳大利亚人口的增长和城市化的快速发展，维持我们的水质是一项艰巨的任务。为了分析得出有效的结论，从而为水管理提供有用的建议，必须开发一种适当的统计方法来分析水质数据。本文旨在开发基于鲁棒性的程序，以分析随时间推移在不同站点收集的非正态分布数据。考虑到站点内观测值的时间相关性，我们考虑了Wang和Zhu（Biometrika，93：459–464，2006）提出的最优组合估计函数，该函数可以更有效地进行参数估计。此外，我们应用诱导平滑方法来减少计算负担。平滑处理可以轻松地计算参数估计值及其方差-协方差矩阵。对来自全铁和总蓝藻的水质数据的分析表明，传统的广义线性混合模型与秩回归模型之间存在差异。我们的分析还证明了秩回归模型在分析非正态数据方面的优势。

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