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；
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。
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。
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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