计算标准误差。
Computation of standard errors.
出版信息
Health Serv Res. 2014 Apr;49(2):731-50. doi: 10.1111/1475-6773.12122.
OBJECTIVES
We discuss the problem of computing the standard errors of functions involving estimated parameters and provide the relevant computer code for three different computational approaches using two popular computer packages.
STUDY DESIGN
We show how to compute the standard errors of several functions of interest: the predicted value of the dependent variable for a particular subject, and the effect of a change in an explanatory variable on the predicted value of the dependent variable for an individual subject and average effect for a sample of subjects. EMPIRICAL APPLICATION: Using a publicly available dataset, we explain three different methods of computing standard errors: the delta method, Krinsky–Robb, and bootstrapping. We provide computer code for Stata 12 and LIMDEP 10/NLOGIT 5.
CONCLUSIONS
In most applications, choice of the computational method for standard errors of functions of estimated parameters is a matter of convenience. However, when computing standard errors of the sample average of functions that involve both estimated parameters and nonstochastic explanatory variables, it is important to consider the sources of variation in the function's values.
目的
我们讨论了计算涉及估计参数的函数的标准误差的问题，并为使用两个流行的计算机程序包的三种不同计算方法提供了相关的计算机代码。
研究设计
我们展示了如何计算几个感兴趣的函数的标准误差：特定主体的因变量的预测值，以及解释变量的变化对单个主体的因变量的预测值和主体样本的平均效应的影响。
实证应用
使用公开可用的数据集，我们解释了计算标准误差的三种不同方法：Delta 方法、Krinky-Robb 和自举法。我们提供了 Stata 12 和 LIMDEP 10/NLOGIT 5 的计算机代码。
结论
在大多数应用中，选择计算估计参数函数的标准误差的方法是一个便利性问题。然而，当计算涉及估计参数和非随机解释变量的函数的样本平均值的标准误差时，重要的是要考虑函数值的变化来源。
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