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首页> 外文期刊>The British journal of mathematical and statistical psychology >On the asymptotic standard error of a class of robust estimators of ability in dichotomous item response models
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On the asymptotic standard error of a class of robust estimators of ability in dichotomous item response models

机译：二分项目反应模型中一类能力的鲁棒估计的渐近标准误差

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摘要

In item response theory, the classical estimators of ability are highly sensitive to response disturbances and can return strongly biased estimates of the true underlying ability level. Robust methods were introduced to lessen the impact of such aberrant responses on the estimation process. The computation of asymptotic (i.e., large-sample) standard errors (ASE) for these robust estimators, however, has not yet been fully considered. This paper focuses on a broad class of robust ability estimators, defined by an appropriate selection of the weight function and the residual measure, for which the ASE is derived from the theory of estimating equations. The maximum likelihood (ML) and the robust estimators, together with their estimated ASEs, are then compared in a simulation study by generating random guessing disturbances. It is concluded that both the estimators and their ASE perform similarly in the absence of random guessing, while the robust estimator and its estimated ASE are less biased and outperform their ML counterparts in the presence of random guessing with large impact on the item response process.
机译：在项目响应理论中，经典的能力估计器对响应扰动高度敏感，并且可以返回真实基础能力水平的强烈偏差估计。引入了鲁棒的方法来减轻这种异常响应对估计过程的影响。但是，尚未充分考虑这些鲁棒估计量的渐近（即大样本）标准误（ASE）的计算。本文着重于一类鲁棒的能力估计器，它由权重函数和残差度量的适当选择所定义，为此，ASE是从估计方程的理论中得出的。然后，在模拟研究中通过产生随机猜测干扰来比较最大似然（ML）和鲁棒估计量以及它们的估计ASE。结论是，在没有随机猜测的情况下，估计器及其ASE的性能相似，而在存在随机猜测的情况下，鲁棒估计器及其估计的ASE的偏差较小，并且胜过其ML对应项，这对项目响应过程有很大影响。

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