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A Note on Applying the BCH Method Under Linear Equality and Inequality Constraints

机译:关于在线性平等和不等式约束下应用BCH方法的说明

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摘要

Researchers often wish to relate estimated scores on latent variables to exogenous covariates not previously used in analyses. The BCH method corrects for asymptotic bias in estimates due to these scores' uncertainty and has been shown to be relatively robust. When applying the BCH approach however, two problems arise. First, negative cell proportions can be obtained. Second, the approach cannot deal with situations where marginals need to be fixed to specific values, such as edit restrictions. The BCH approach can handle these problems when placed in a framework of quadratic loss functions and linear equality and inequality constraints. This research note gives the explicit form for equality constraints and demonstrates how solutions for inequality constraints may be obtained using numerical methods.
机译:研究人员通常希望将估计的分数与以前在分析中以前未使用的外源协变量相关。 由于这些分数的不确定性,BCH方法校正了估计中的渐近偏差,并且已被证明是相对稳健的。 然而,在施加BCH方法时,出现了两个问题。 首先,可以获得负细胞比例。 其次,该方法无法应对边际需要修复到特定值的情况,例如编辑限制。 当放置在二次丢失功能和线性平等和不等式约束的框架中,BCH方法可以处理这些问题。 该研究说明提供了平等约束的显式形式,并演示了如何使用数值方法获得对不等式约束的解决方案。

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