平均分位数、方差收缩和过度自信

Roger M. Cooke
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引用次数: 1

摘要

平均分位数作为一种结合专家判断的方法,从数学和实证两个方面进行了研究。分位数平均相当于取在分位数点评估的密度的谐波平均值。在等权重和调和权重之间建立了方差收缩定律。对49项2006年后研究的数据进行了扩展,除平等加权和基于绩效的加权外,还包括谐波加权。谐波加权具有最高的平均信息和降低的统计精度。在28项研究中,分位数平均值在统计上准确的假设在5%的水平上被拒绝,在15项研究中在0.1%的水平上则被拒绝。对于性能加权,这些数字为3和1,对于相等的加权为2和1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Averaging quantiles, variance shrinkage, and overconfidence

Averaging quantiles as a way of combining experts' judgments is studied both mathematically and empirically. Quantile averaging is equivalent to taking the harmonic mean of densities evaluated at quantile points. A variance shrinkage law is established between equal and harmonic weighting. Data from 49 post-2006 studies are extended to include harmonic weighting in addition to equal and performance-based weighting. It emerges that harmonic weighting has the highest average information and degraded statistical accuracy. The hypothesis that the quantile average is statistically accurate would be rejected at the 5% level in 28 studies and at the 0.1% level in 15 studies. For performance weighting, these numbers are 3 and 1, for equal weighting 2 and 1.

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