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Asymptotic properties of sample quantiles of discrete distributions

机译:离散分布样本分位数的渐近性质

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摘要

The asymptotic distribution of sample quantiles in the classical definition is well-known to be normal for absolutely continuous distributions. However, this is no longer true for discrete distributions or samples with ties. We show that the definition of sample quantiles based on mid-distribution functions resolves this issue and provides a unified framework for asymptotic properties of sample quantiles from absolutely continuous and from discrete distributions. We demonstrate that the same asymptotic normal distribution result as for the classical sample quantiles holds at differentiable points, whereas a more general form arises for distributions whose cumulative distribution function has only one-sided differentiability. For discrete distributions with finite support, the same type of asymptotics holds and the sample quantiles based on mid-distribution functions either follow a normal or a two-piece normal distribution. We also calculate the exact distribution of these sample quantiles for the binomial and Poisson distributions. We illustrate the asymptotic results with simulations.
机译:众所周知,经典分位数中样本分位数的渐近分布对于绝对连续分布而言是正态的。但是,这对于离散分布或有联系的样本不再适用。我们表明,基于中间分布函数的样本分位数的定义解决了此问题,并为绝对连续和离散分布的样本分位数的渐近性质提供了统一的框架。我们证明,与经典样本分位数相同的渐近正态分布结果在可微点上成立,而对于累积分布函数仅具有一侧微分的分布,则出现了一种更通用的形式。对于具有有限支持的离散分布,保持相同类型的渐近性,并且基于中间分布函数的样本分位数遵循正态分布或两件式正态分布。我们还针对二项分布和泊松分布计算了这些样本分位数的精确分布。我们通过仿真来说明渐近结果。

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