We first develop the tests for varying dispersion in two special generalized nonlinear models of longitudinal data: (1) logistic nonlinear models in binomial data;

首先刻画了非线性随机效应模型的异方差类型，进而研究了非线性随机效应模型的异方差检验；

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The proposed model-calibration estimators can handle any linear or nonlinear working models and reduce to the generalized regression estimators in the linear model case.

建议的模型校正估计可以处理任何线性或非线性的工作模型，且在线性模型的情形下变为广义回归估计。

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For generalized linear and nonlinear models, it is still a conventional hypothesis that observed data should have nominal dispersion, otherwise statistical inference would be more difficult.

对于广义回归模型，人们也总是假设数据具有名义离差，否则，统计推断更加困难。

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