Stating a confidence interval is a traditional method of indicating the sampling error of a point estimator of a model's performance measure. We propose a single dimensionless criterion, inspired by Schruben's coverage function, for evaluating and comparing the statistical quality of confidence-interval procedures. Procedure quality is usually thought to be multidimensional, composed of the mean (and maybe the variance) of the interval-width distribution and the probability of covering the performance measure (and maybe other values). Our criterion, which we argue lies at the heart of what makes a confidence-interval procedure good or bad, compares a given procedure's intervals to those of an "ideal" procedure. For a given point estimator (such as the sample mean) and given experimental data process (such as a first-order autoregressive process with specified parameters), our single criterion is a function of only the sample size (or other rule that ends sampling).
|Number of pages||8|
|Journal||Winter Simulation Conference Proceedings|
|State||Published - 2002|
|Event||Proceedings of the 2002 Winter Simulation Conference - San Diego, CA, United States|
Duration: 8 Dec 2002 → 11 Dec 2002