TESTING THE FIT OF A REGRESSION-MODEL VIA SCORE TESTS IN RANDOM EFFECTS MODELS
TESTING THE FIT OF A REGRESSION-MODEL VIA SCORE TESTS IN RANDOM EFFECTS MODELS
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DOI：
10.2307/2532948
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发表时间：
1995-06-01
期刊：
影响因子：
1.9
通讯作者：
VANHOUWELINGEN, HC
中科院分区：
文献类型：
--
作者：
LECESSIE, S;VANHOUWELINGEN, HC
本文研究回归模型拟合优度的检验问题。重点讨论了具有典型联系函数和已知离差参数的广义线性模型的拟合优度检验。该检验基于随机效应模型中额外变异的得分检验。通过选择一种合适的色散矩阵形式，得到了一种与基于非参数核方法的检验统计量非常相似的拟合优度检验统计量。我们考虑了检验统计量的分布，并讨论了离差矩阵的选择。该检验方法可以处理具有连续和离散协变量的模型。估计参数时的偏差校正是可用的，扩展到模型与未知的色散参数，更一般的非线性模型进行了讨论。建议的拟合优度的方法证明在模拟研究和骨髓移植患者的真实的数据。观测值对检验统计量的个体贡献用于执行残差分析。
This paper considers testing the goodness of fit of regression models. Emphasis is on a goodness-of-fit test for generalized linear models with canonical link function and known dispersion parameter. The test is based on the score test for extra variation in a random effects model. By choosing a suitable form for the dispersion matrix, a goodness-of-fit test statistic is obtained which is quite similar to test statistics based on non-parametric kernel methods. We consider the distribution of the test statistic and discuss the choice of the dispersion matrix. The testing method can handle models with continuous and discrete covariates. Corrections for bias when parameters are estimated are available and extensions to models with unknown dispersion parameters, and more general nonlinear models are discussed. The proposed goodness-of-fit method is demonstrated in a simulation study and on real data of bone marrow transplant patients. The individual contributions of observations to the test statistic are used to perform residual analyses.