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In many statistical problems, several estimators are usually available forinterval estimation of a parameter of interest, and hence, the selection of anappropriate estimator is important. The criterion for a good estimator is tohave a high coverage probability close to the nominal level and a shorterinterval length. However, these two concepts are in opposition to each other:high and low coverages are associated with longer and shorter interval lengthsrespectively. Some methods, such as bootstrap calibration, modify the nominallevel to improve the coverage and thereby allow the selection of intervalsbased on interval lengths only. Nonetheless, these methods are computationallyexpensive. In this paper, we propose an index which offers an easy to computeapproach of comparing confidence interval estimators based on a compromisebetween the coverage probability and the confidence interval length. Weillustrate that the confidence interval index has range of values within theneighborhood of the range of the coverage probability, [0,1]. In addition, agood confidence interval estimator has an index value approaching 1; and a badconfidence interval has an index value approaching 0. A simulation study wasconducted to assess the finite sample performance of the index. The proposedindex is illustrated with a practical example from the literature

Comparison of Confidence Interval Estimators: an Index Approach