从相关性中获益：分类数据的调整估计量
Profiting from correlations: Adjusted estimators for categorical data
摘要：
为了考虑样本偏差和偏度，从业者经常根据某种边缘分布来衡量他们的观察结果。本文表明，这种加权确实可以提高估计。研究列联表，在另一个边际已知的假设下，提出了边缘分布的估计。结果表明，当两个边缘值相关时，加权估计具有严格较小的渐近方差。有限样本性能的仿真研究中示出。作为交通事故数据的应用程序，该方法允许纠正一个众所周知的偏见，在观察到的伤害严重程度分布。
英文摘要：
To take sample biases and skewness in the observations into account, practitioners frequently weight their observations according to some marginal distribution. The present paper demonstrates that such weighting can indeed improve the estimation. Studying contingency tables, estimators for marginal distributions are proposed under the assumption that another marginal is known. It is shown that the weighted estimators have a strictly smaller asymptotic variance whenever the two marginals are correlated. The finite sample performance is illustrated in a simulation study. As an application to traffic accident data the method allows for correcting a well-known bias in the observed injury severity distribution.
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