Linear mixed models and penalized least squares
Linear mixed models and penalized least squares
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DOI：
10.1016/j.jmva.2004.04.013
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发表时间：
2004-10-01
影响因子：
1.6
通讯作者：
DebRoy, S
中科院分区：
文献类型：
--
作者：
Bates, DM;DebRoy, S
线性混合效应模型是一类重要的统计模型，直接用于许多应用领域，也用作拟合其他类型混合效应模型（如广义线性混合模型）的迭代步骤。这些模型中的参数通常用最大似然或受限最大似然来估计。一般来说，这些估计没有封闭形式的解，它们必须由迭代算法确定，如EM迭代或一般非线性优化。这种迭代的许多中间计算已被表示为广义最小二乘问题。我们表明，作为惩罚最小二乘问题的替代表示具有许多有利的计算特性，包括显式评估轮廓对数似然或对数限制似然的能力，该轮廓目标的梯度和Hessian，以及改进该目标的ECME更新。(C) 2004爱思唯尔公司版权所有。
Linear mixed-effects models are an important class of statistical models that are used directly in many fields of applications and also are used as iterative steps in fitting other types of mixed-effects models, such as generalized linear mixed models. The parameters in these models are typically estimated by maximum likelihood or restricted maximum likelihood. In general, there is no closed-form solution for these estimates and they must be determined by iterative algorithms such as EM iterations or general nonlinear optimization. Many of the intermediate calculations for such iterations have been expressed as generalized least squares problems. We show that an alternative representation as a penalized least squares problem has many advantageous computational properties including the ability to evaluate explicitly a profiled log-likelihood or log-restricted likelihood, the gradient and Hessian of this profiled objective, and an ECME update to refine this objective. (C) 2004 Elsevier Inc. All rights reserved.