Saddlepoint Approximations for the Difference of Order Statistics
研究了连续分布中次序统计量之差的分布鞍点逼近方法，以指数分布和非中心卡方分布为例，证明该逼近在样本量适中时远优于正态逼近，适用于时间序列线性检验等问题。
Saddlepoint approximations for the distribution of the difference of order statistics from a continuous distribution are presented. The approximations are illustrated for the case of the exponential distribution where we can find the exact probability density function of the difference of order statistics, and for the case of the noncentral chi-squared distribution which has application to the problem of testing a time series for linearity. In both cases we show that the saddlepoint approximation is much better than the normal approximation, especially for realistic sample sizes.