Functional mixed effects models
Functional mixed effects models
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DOI：
10.1002/wics.1226
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发表时间：
2012-11
期刊：
影响因子：
--
通讯作者：
Ziyue Liu;Wensheng Guo
中科院分区：
文献类型：
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作者：
Ziyue Liu;Wensheng Guo
功能混合效应模型（FMM）是一种将固定效应和随机效应都用非参数曲线建模的混合效应建模框架。混合效应模型和非参数平滑的结合使FERNING能够处理具有复杂特征的结果，同时纳入复杂的实验设计并包含协变量。可以使用线性混合效应模型的技术或使用完全贝叶斯方法进行估计和推断。与函数数据分析一样，Festival中的推理是初步的，需要进一步研究。已经开发了几个软件包来实现FRESH，尽管无论使用哪种平滑方法都存在计算挑战。WIREs Comput Stat 2012，4：527-534。doi：10.1002/wics.1226
Functional mixed effects model (FMM) is a mixed effects modeling framework that both the fixed effects and the random effects are modeled by nonparametric curves. The combination of mixed effects model and nonparametric smoothing enables FMMs to handle outcomes with complex profiles and at the same time to incorporate complex experimental designs and include covariates. Estimation and inference can be performed either using techniques from linear mixed effects models or using fully Bayesian approaches. As in functional data analysis, inference in FMMs is preliminary and needs to be further investigated. Several software packages have been developed to implement FMMs, although computational challenges do exist no matter which smoothing method is used. WIREs Comput Stat 2012, 4:527–534. doi: 10.1002/wics.1226