泊松模型与自举的SE中的标准误差
我试图比较两种方法中的SES：
- 在GLM Poisson模型中的结果部分中打印的SE，与
- 我从引导中获得的系数的SE相比（基本上，我用更换重新采样了相同数量的观察值，并适合一个新的观察值泊松模型并获取系数，然后我从1000个引导程序中计算出系数的SD），
但是两种方法中的SES却大不相同。 1）的SE小得多。我认为它们在数字上会很相似。
有人有解释吗？
I was trying to compare the SEs from two approaches:
- the SE printed in the results section in the GLM poisson model, vs.
- the SE of the coefficient I got from bootstrapping (basically I resampled the same number of observations with replacement and fit a new Poisson model and get the coefficient, then I compute the SD of the coefficients from 1000 bootstraps)
However the SEs from the two approaches are quite different. The SE from the 1) is much smaller. I thought they would be numerically similar.
Anyone has a explanation?
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如果没有可重现的例子，很难回答。通常的怀疑是响应变量中存在过度分散，导致常规的标准错误太小。替代方案包括以下标准错误：Bootstrap，Sandwich，Quasi-Poisson或拟合负二项式模型。
参见有关R的工作示例以及第7.4章， https://discdown.org/microecormentics/ 。这些使用
sandwich（）函数来自软件包sandwich，该功能还包括Bootstrap协方差的函数vcovbs（）。Hard to answer without a reproducible example. The usual suspicion would be that there is overdispersion in the response variable, leading to conventional standard errors that are too small. Alternatives include standard errors from: bootstrap, sandwich, quasi-Poisson, or fitting a negative binomial model instead.
See Section 3 of https://doi.org/10.18637/jss.v027.i08 for a worked example in R as well as Chapter 7.4 in https://discdown.org/microeconometrics/. These use the
sandwich()function from the packagesandwichwhich also includes a functionvcovBS()for bootstrap covariances.