Jackknifed Ratio Estimation in Statistical Auditing
提出刀切比率估计法，通过样本分割直接估计方差，提高审计中总体总额点估计和区间估计的精度，但无法完全解决低错误率或单侧错误总体的置信区间不可靠问题。
In auditing of an accounting population, auditors are concerned with the precision of both a point estimate and an interval estimate of the true value of the population total. A ratio estimator is efficient for obtaining a point estimate, but does not always produce a reliable interval estimate. It is well known that the estimated variance of the ratio estimator is biased. Since this is a possible cause of the unreliable confidence interval estimates, a jackknifed ratio estimator is suggested and tested. With the jackknifed ratio estimator, the variance of the estimator is obtained directly from the sample by means of sample splitting. Our conclusion, based on an extensive Monte Carlo study, is that the jackknife improves the accuracy of the point estimate and the reliability of the interval estimate. The improvement is not great enough to overcome the primary deficiency of the ratio estimator-the unreliability of the confidence interval when the population has either low error rates or one-sided errors; however, the jackknife estimator dominates the ratio estimator in those populations that are most favorable for applying the ratio estimator, that is, populations with high error rates and both over- and understatement errors.