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首页> 外文期刊>Journal of applied statistics >Multilevel modelling of survey data: impact of the two-level weights used in the pseudolikelihood
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Multilevel modelling of survey data: impact of the two-level weights used in the pseudolikelihood

机译：调查数据的多级建模：伪似然中使用的两级权重的影响

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摘要

Approaches that use the pseudolikelihood to perform multilevel modelling on survey data have been presented in the literature. To avoid biased estimates due to unequal selection probabilities, conditional weights can be introduced at each level. Less-biased estimators can also be obtained in a two-level linear model if the level-1 weights are scaled. In this paper, we studied several level-2 weights that can be introduced into the pseudolikelihood when the sampling design and the hierarchical structure of the multilevel model do not match. Two-level and three-level models were studied. The present work was motivated by a study that aims to estimate the contributions of lead sources to polluting the interior floor dust of the rooms within dwellings. We performed a simulation study using the real data collected from a French survey to achieve our objective. We conclude that it is preferable to use unweighted analyses or, at the most, to use conditional level-2 weights in a two-level or a three-level model. We state some warnings and make some recommendations.
机译：文献中提出了使用伪似然法对调查数据执行多级建模的方法。为了避免由于选择概率不相等而造成的估计偏差，可以在每个级别引入条件权重。如果对1级权重进行了缩放，则也可以在两级线性模型中获得较少偏差的估计量。在本文中，我们研究了当多层设计的抽样设计和层次结构不匹配时可以引入伪似然性的几种2级权重。研究了两级和三级模型。当前的工作是出于一项研究的目的，该研究旨在估计铅源对污染住宅内房间内部地板灰尘的贡献。我们使用从法国调查中收集的真实数据进行了模拟研究，以实现我们的目标。我们得出的结论是，最好在两级或三级模型中使用无权分析，或者最多使用条件级2权重。我们声明一些警告并提出一些建议。

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