论文标题
复合可能性的普遍推断
Universal Inference with Composite Likelihoods
论文作者
论文摘要
Wasserman等。 （2020年，PNAS，第117卷，第16880-16890页）构建了估计器不可知论和有限样本的有效置信集和假设检验，并使用基于拆分DATA的可能性比率进行了统计数据。我们证明，相同的方法也扩展到使用拆分数据的复合可能性比率，因此在仅知道数据生成过程的情况下才知道坐标之间的边际和条件关系时，建立了进行多元推断的通用方法。还考虑了始终视频的顺序推断。
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.
