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稀疏网络的拟合优度检验AI 解读

The stochastic block model (SBM) has been widely used to analyze network data. Various goodness-of-fit tests have been proposed to assess the adequacy of model structures. To the best of our knowledge, however, none of the existing approaches are applicable for sparse networks in which the connection probability of any two communities is of order n−1logn, and the number of communities is divergent. To fill this gap, we propose a novel goodness-of-fit test for the stochastic…

高效估计分位数处理效应的协变量调整响应自适应实验AI 解读

In program evaluation studies, understanding the heterogeneous distributional impacts of a program beyond the average effect is crucial. Quantile treatment effect (QTE) provides a natural measure to capture such heterogeneity. While much of the existing work for estimating QTE has focused on analyzing observational data based on untestable causal assumptions, little work has gone into designing randomized experiments specifically for estimating QTE. In this manuscript, we…

高维随机向量相互独立性的秩最大值—求和检验AI 解读

We consider the problem of testing mutual independence of high-dimensional random vectors, and propose a series of high-dimensional rank-based max-sum tests, which are suitable for high-dimensional data and can be robust to distribution types of the variables, form of the dependence between variables and the sparsity of correlation coefficients. Further, we demonstrate the application of some representative members of the proposed tests on testing cross-sectional…

高维面板数据截面独立性的最大值—求和检验AI 解读

We consider a testing problem for cross-sectional independence for high-dimensional panel data, where the number of cross-sectional units is potentially much larger than the number of observations. The cross-sectional independence is described through linear regression models. We study three tests named the sum, the max and the max-sum tests, where the latter two are new. The sum test is initially proposed by Breusch and Pagan (1980). We design the max and sum tests for…