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摘要翻译：
当高斯随机效应的方差矩阵具有规定的零点模式(PPZ)时，我们提出了非线性混合效应模型的极大似然估计(MLE)的一种新方法。该方法将最近发展起来的迭代条件拟合(ICF)算法与期望最大化(EM)算法相结合。它为任何样本量提供正定估计，并且不依赖于对PPZ的任何结构假设。它可以很容易地适应许多版本的EM。
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英文标题：
《A new method for the estimation of variance matrix with prescribed zeros
in nonlinear mixed effects models》
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作者：
Djalil Chafai (UPTE, IMT), Didier Concordet (UPTE, IMT)
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最新提交年份：
2008
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
We propose a new method for the Maximum Likelihood Estimator (MLE) of nonlinear mixed effects models when the variance matrix of Gaussian random effects has a prescribed pattern of zeros (PPZ). The method consists in coupling the recently developed Iterative Conditional Fitting (ICF) algorithm with the Expectation Maximization (EM) algorithm. It provides positive definite estimates for any sample size, and does not rely on any structural assumption on the PPZ. It can be easily adapted to many versions of EM.
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PDF链接：
https://arxiv.org/pdf/709.0111
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