We present a method for computing optimal fixed-width confidence intervals for a single, bounded parameter, extending a method for the binomial due to Asparaouhov and Lorden, who called it the Push algorithm. The method produces the shortest possible non-decreasing confidence interval for a given confidence level, and if the Push interval does not exist for a given width and level, then no such interval exists. The method applies to any bounded parameter that is discrete, or is continuous and has the monotone likelihood ratio property. We demonstrate the method on the binomial, hypergeometric, and normal distributions with our available R package. In each of these distributions the proposed method outperforms the standard ones, and in the latter case even improves upon the $z$-interval. We apply the proposed method to World Health Organization (WHO) data on tobacco use.

翻译：我们提出了一种计算单个有界参数的最优固定宽度置信区间的方法，该方法扩展了Asparaouhov和Lorden针对二项分布提出的方法（他们称之为Push算法）。该方法在给定置信水平下生成最短的非递减置信区间，若对于特定宽度和水平不存在Push区间，则表明此类区间不存在。该方法适用于任何离散的有界参数，或具有单调似然比性质的连续有界参数。我们通过可用的R包在二项分布、超几何分布和正态分布上验证了该方法。在这些分布中，所提方法均优于标准方法，在正态分布中甚至改进了$z$区间。我们将所提方法应用于世界卫生组织（WHO）的烟草使用数据。