幸福结果的稳健排名：中位数回归视角
Robust Ranking of Happiness Outcomes: A Median Regression Perspective
摘要：
有序概率和logit模型经常被用来估计各组之间幸福结果（和其他有序数据）的平均排名。然而，最近有人强调，在大多数幸福应用程序中可能无法识别这种排名。我们建议研究人员关注中位数比较，而不是平均值。这是因为即使平均秩不能被识别，中位数秩也可以被识别。此外，使用标准统计软件可以很容易地估计概率单位和logit模型中的中位数。中位数的排名，以及其他分位数的排名，也可以估计半参数，我们提供了一个新的约束混合整数优化程序的实施。我们应用它来估计一个幸福方程使用美国的综合社会调查数据。
英文摘要：
Ordered probit and logit models have been frequently used to estimate the mean ranking of happiness outcomes (and other ordinal data) across groups. However, it has been recently highlighted that such ranking may not be identified in most happiness applications. We suggest researchers focus on median comparison instead of the mean. This is because the median rank can be identified even if the mean rank is not. Furthermore, median ranks in probit and logit models can be readily estimated using standard statistical softwares. The median ranking, as well as ranking for other quantiles, can also be estimated semiparametrically and we provide a new constrained mixed integer optimization procedure for implementation. We apply it to estimate a happiness equation using General Social Survey data of the US.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
