MOVER-R and Penalized MOVER-R Confidence Intervals for the Ratio of Two Quantities

Peng Wang, Yilei Ma, Siqi Xu, Yi-Xin Wang, Yu Zhang, Xiangyang Lou, Ming Li, Baolin Wu, Guimin Gao, Ping Yin and Nianjun Liu

The American Statistician, 2023, vol. 77, issue 4, 381-389

Abstract: Developing a confidence interval for the ratio of two quantities is an important task in statistics because of its omnipresence in real world applications. For such a problem, the MOVER-R (method of variance recovery for the ratio) technique, which is based on the recovery of variance estimates from confidence limits of the numerator and the denominator separately, was proposed as a useful and efficient approach. However, this method implicitly assumes that the confidence interval for the denominator never includes zero, which might be violated in practice. In this article, we first use a new framework to derive the MOVER-R confidence interval, which does not require the above assumption and covers the whole parameter space. We find that MOVER-R can produce an unbounded confidence interval, just like the well-known Fieller method. To overcome this issue, we further propose the penalized MOVER-R. We prove that the new method differs from MOVER-R only at the second order. It, however, always gives a bounded and analytic confidence interval. Through simulation studies and a real data application, we show that the penalized MOVER-R generally provides a better confidence interval than MOVER-R in terms of controlling the coverage probability and the median width.

Date: 2023
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DOI: 10.1080/00031305.2023.2173294

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