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arXiv 1912.04089math.STstat.TH
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线性混合效应模型函数形式的拟合优度检验

Goodness-of-fit tests for functional form of Linear Mixed effects Models

Rok Blagus, Jakob Peterlin, Nataša Kejžar

摘要:

线性混合效应模型(Linear mixed effects models,缩写为LM)是一种流行且强大的工具,用于分析数值结果的聚类或重复观测。线性矩阵由固定和随机分量组成,通过其各自的设计矩阵在模型中指定。检查两个设计矩阵是否正确指定是至关重要的,因为错误指定它们会影响分析的有效性和效率。我们展示了如何使用定义为适当有序模型残差的累积和的随机过程来测试拟合LMM的函数形式是否正确指定。我们展示了如何使用这些过程来测试整个模型的函数形式的拟合拟合优度,或者仅测试其固定和/或随机分量。所提出的过程的检查图被证明是高度翔实的潜在的错误规格的功能形式的模型,提供线索的潜在改进模型的拟合。我们展示了如何通过使用一种新的估计$p$ -值的程序,它可以是基于符号翻转/自举或模拟,并通过使用理论结果和一个大的Monte Carlo模拟研究显示其有效性的目视检查可以是客观的。所提出的方法可用于多水平或交叉随机效应的线性回归分析。并且还可以扩展到广义的Lebanon。

英文摘要:

Linear mixed effects models (LMMs) are a popular and powerful tool for analyzing clustered or repeated observations for numeric outcomes. LMMs consist of a fixed and a random component, specified in the model through their respective design matrices. Checking if the two design matrices are correctly specified is crucial since mis-specifying them can affect the validity and efficiency of the analysis. We show how to use random processes defined as cumulative sums of appropriately ordered model's residuals to test if the functional form of the fitted LMM is correctly specified. We show how these processes can be used to test goodness-of-fit of the functional form of the entire model, or only its fixed and/or random component. Inspecting plots of the proposed processes is shown to be highly informative about the potential mis-specification of the functional form of the model, providing clues for potential improvement of the model's fit. We show how the visual inspection can be objectified by using a novel procedure for estimating $p$-values which can be based on sign-flipping/bootstrap or simulations and show its validity by using theoretical results and a large Monte Carlo simulation study. The proposed methodology can be used with LMMs with multi-level or crossed random effects.% and could also be extended to generalized LMMs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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