AI 中文总结
本文针对无向二元数据的线性条件均值模型，提出了修正的高斯自助法辅助的综合性设定检验，经模拟及Lazega律师事务所网络应用验证，其规模控制稳定且功效显著。
AI 中文摘要
本文针对具有无向二元数据的线性条件均值模型，提出了综合性设定检验方法。我们建立了一致投影定理，在共享节点依赖下将二元过程约简为其潜在的一阶节点投影。随后证明，当该节点分量非退化时，原始的一阶节点乘子自助法有效，但在二元独立时会重复计算二元特有的变异。精确协方差分解启发了一种修正的高斯自助法，该方法在两种情形下均有效。由此得到的柯尔莫哥洛夫-斯米尔诺夫检验和克拉默-冯·米塞斯检验，对固定备择假设具有一致性，对速率适配的局部备择假设具有非平凡功效。模拟结果显示，修正后的柯尔莫哥洛夫-斯米尔诺夫检验在保持显著局部功效的同时，提供了最稳定的规模控制。将其应用于Lazega律师事务所网络，结果拒绝了加性线性和二次设定，但在纳入具有经济意义的交互项后，未发现剩余的设定误设。
英文摘要
This paper develops omnibus specification tests for linear conditional-mean models with undirected dyadic data. We establish a uniform projection theorem that reduces the dyadic process to its latent first-order node projections under shared-node dependence. We then show that a raw first-order node-multiplier bootstrap is valid when this node component is nondegenerate but double-counts dyad-specific variation when dyads are independent. An exact covariance decomposition motivates a corrected Gaussian bootstrap that is valid in both regimes. The resulting Kolmogorov-Smirnov and Cramér-von Mises tests are consistent against fixed alternatives and have nontrivial power against rate-appropriate local alternatives. Simulations show that the corrected Kolmogorov-Smirnov test provides the most stable size control while retaining substantial local power. An application to the Lazega law-firm network rejects additive linear and quadratic specifications but finds no remaining misspecification after including an economically relevant interaction.
