Pseudo R-squared measures for Poisson regression models with over- or underdispersion
Pseudo R-squared measures for Poisson regression models with over- or underdispersion
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DOI：
10.1016/s0167-9473(03)00062-8
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发表时间：
2003-10-28
影响因子：
1.8
通讯作者：
Mittlböck, M
中科院分区：
文献类型：
--
作者：
Heinzl, H;Mittlböck, M
泊松回归模型常用来分析计数数据。最近提出了泊松回归模型的伪r平方度量，并建议在存在小样本和/或大量协变量的情况下进行偏差调整。然而，在实践中，与标准泊松模型相比，数据往往过于分散，有时甚至分散不足。泊松r平方度量的定义也可以应用于这些情况，尽管有相应的偏差调整。这些调整的动机是准似然理论的论点。在标准泊松条件下，通过仿真研究了未调整和调整r平方测度的性质；以爆米花数据为例，讨论了过分散和欠分散泊松回归模型及其应用。(C) 2003 Elsevier B.V.版权所有
The Poisson regression model is frequently used to analyze count data. Pseudo R-squared measures for Poisson regression models have recently been proposed and bias adjustments recommended in the presence of small samples and/or a large number of covariates. In practice, however, data are often over- or sometimes even underdispersed as compared to the standard Poisson model. The definition of Poisson R-squared measures can be applied in these situations as well, albeit with bias adjustments accordingly adapted. These adjustments are motivated by arguments of quasi-likelihood theory. Properties of unadjusted and adjusted R-squared measures are studied by simulation under standard Poisson; over- and underdispersed Poisson regression models and their use is exemplified and discussed with popcorn data. (C) 2003 Elsevier B.V. All rights reserved.