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敏感性分析中的渐近可分性AI 解读

Summary In an observational study in which each treated subject is matched to several untreated controls by using observed pretreatment covariates, a sensitivity analysis asks how hidden biases due to unobserved covariates might alter the conclusions. The bounds required for a sensitivity analysis are the solution to an optimization problem. In general, this optimization problem is not separable, in the sense that one cannot find the needed optimum by performing a separate…

观察性研究中双样本置换推断的敏感性AI 解读

In observational studies, subjects are not randomly assigned to treatment or control, so they may differ in their chances of receiving the treatment. A simple method is developed and demonstrated for displaying the sensitivity of conventional two-group permutation inferences to departures from random assignment of treatments. The unmatched case, discussed here, differs in certain technical and computational details from the matched case, discussed previously; however, the…

观察性研究中两样本置换推断的敏感性AI 解读

In observational studies, subjects are not randomly assigned to treatment or control, so they may differ in their chances of receiving the treatment. A simple method is developed and demonstrated for displaying the sensitivity of conventional two-group permutation inferences to departures from random assignment of treatments. The unmatched case, discussed here, differs in certain technical and computational details from the matched case, discussed previously; however, the…

分组数据估计的基尼及相关不平等指数界限的大样本理论AI 解读

The joint asymptotic distribution of the upper and lower bounds for the Gini index derived by Gastwirth for grouped data are obtained. From them a conservative asymptotically distribution-free confidence interval for the population Gini index is presented. The methods also yield similar results for other indices of inequality (e.g., Theil's and Atkinson's).

单峰密度下由分组数据界定矩、基尼指数与洛伦兹曲线AI 解读

In this article we first show how to extend the method for bounding ∫ h(x)f(x)dx from data grouped in intervals in which h(x) is convex and f(x) is decreasing to the case in which f(x) is unimodal. We then show that the unimodal density functions that bound ∫ h(x)f(x)dx for any convex function h(x) also bound the Gini index and the Lorenz curve.

利用分组数据界定分布矩AI 解读

This article derives bounds on a wide class of moments of the underlying distribution from data grouped in intervals when the mean of each group is given. The extremal distributions are constructed yielding the upper and lower bounds to moments of the form ∫h(x)dF(x), where h(x) is convex and F(x) is concave. To illustrate the results, bounds on the variance and Gini index are presented for two theoretical distributions.