Fast and reliable confidence intervals for a variance component
提出一种基于受限似然得分检验的方差分量置信区间方法，在参数边界附近仍保持渐近正确覆盖率，计算速度比常用方法快2到28000倍，并在空间转录组学中用于快速基因排序。
Summary We show that in a variance component model, confidence intervals with asymptotically correct uniform coverage probability can be obtained by inverting certain test statistics based on the score for the restricted likelihood. The results hold in settings where the variance component is near or at the boundary of the parameter set. Simulations indicate that the proposed test statistics are approximately pivotal and lead to confidence intervals with near-nominal coverage even in small samples. We illustrate the application of the proposed methods in spatially resolved transcriptomics, where we compute approximately 15 000 confidence intervals, used for gene ranking, in less than 4 minutes. In the settings we consider, the proposed method is between two and 28 000 times faster than popular alternatives, depending on how many confidence intervals are computed.