Quantile regression reveals hidden bias and uncertainty in habitat models
Quantile regression reveals hidden bias and uncertainty in habitat models
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DOI：
10.1890/04-0785
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发表时间：
2005-03-01
期刊：
影响因子：
4.8
通讯作者：
Flather, CH
中科院分区：
文献类型：
--
作者：
Cade, BS;Noon, BR;Flather, CH
我们模拟了缺失信息对动物响应统计分布的影响，这些统计分布与栖息地的测量预测因子存在相关性，以评估分位数回归的实用性和性能，以提供更有用的栖息地关系不确定性区间。这些程序是在与与其他重要过程相关的缺失变量混淆而导致异质性和隐藏偏差的情况下进行评估的，这是生态现象统计建模中常见的问题。对代表景观上网格位置的大型(N=10000)有限种群的模拟表明，当一个已测量的栖息地变量对某些动物的影响与另一个未测量变量的影响相混淆时，可能会出现各种形式的隐藏偏差。分位数(0
We simulated the effects of missing information on statistical distributions of animal response that covaried with measured predictors of habitat to evaluate the utility and performance of quantile regression for providing more useful intervals of uncertainty in habitat relationships. These procedures were evaulated for conditions in which heterogeneity and hidden bias were induced by confounding with missing variables associated with other improtant processes, a problem common in statistical modeling of ecological phenomena. Simulations for a large (N = 10000) finite population representing grid locations on a landscape demonstrated various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable. Quantile (0