Relative Performance of Categorical Diagonally Weighted Least Squares and Robust Maximum Likelihood Estimation
Relative Performance of Categorical Diagonally Weighted Least Squares and Robust Maximum Likelihood Estimation
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DOI：
10.1080/10705511.2014.859510
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发表时间：
2014-01-02
影响因子：
6
通讯作者：
Bandalos, Deborah L.
中科院分区：
文献类型：
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作者：
Bandalos, Deborah L.
鲁棒最大似然 (ML) 和分类对角加权最小二乘 (cat-DWLS) 估计都被提议用于分类和非正态分布数据。本研究在参数估计和标准误差偏差、功效和 I 类误差控制方面比较了 2 种方法的结果，并包含未经调整的 ML 和 WLS 估计方法以进行比较。操纵的条件包括模型错误指定、不对称程度、级别和分类、样本大小以及模型的类型和大小。结果表明，在大多数研究条件下，cat-DWLS 估计方法产生的参数估计和标准误差偏差最小。 Cat-DWLS 参数估计和标准误差通常受所研究的估计方法的模型错误指定的影响最小。稳健的机器学习也表现良好，产生相对无偏的参数估计和标准误差。然而，在数据高度不对称、样本量较小和模型轻度指定错误的情况下，cat-DWLS 和稳健的 ML 都会导致功效较低。对于更优化的条件，这些估计器的功效是足够的。
Robust maximum likelihood (ML) and categorical diagonally weighted least squares (cat-DWLS) estimation have both been proposed for use with categorized and nonnormally distributed data. This study compares results from the 2 methods in terms of parameter estimate and standard error bias, power, and Type I error control, with unadjusted ML and WLS estimation methods included for purposes of comparison. Conditions manipulated include model misspecification, level of asymmetry, level and categorization, sample size, and type and size of the model. Results indicate that cat-DWLS estimation method results in the least parameter estimate and standard error bias under the majority of conditions studied. Cat-DWLS parameter estimates and standard errors were generally the least affected by model misspecification of the estimation methods studied. Robust ML also performed well, yielding relatively unbiased parameter estimates and standard errors. However, both cat-DWLS and robust ML resulted in low power under conditions of high data asymmetry, small sample sizes, and mild model misspecification. For more optimal conditions, power for these estimators was adequate.