Fast and reliable confidence intervals for a variance component

Abstract

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.