Wasserman et al. (2020, PNAS, vol. 117, pp. 16880-16890) constructed estimator agnostic and finite-sample valid confidence sets and hypothesis tests, using split-data likelihood ratio-based statistics. We demonstrate that the same approach extends to the use of split-data composite likelihood ratios as well, and thus establish universal methods for conducting multivariate inference when the data generating process is only known up to marginal and conditional relationships between the coordinates. Always-valid sequential inference is also considered.
翻译:Wasserman等人(2020年,PNAS,第117卷,第16880-16890页)利用基于数据概率的多种数据统计,建造了估计性不可知性和有限抽样的有效信任套件和假设测试,我们证明,同一方法也适用于使用多种数据综合概率比率,从而确立了在数据收集过程仅已知在坐标之间处于边际和有条件关系的情况下进行多变量推论的普遍方法,还考虑了始终有效的连续推论。