Fisher Scoring for crossed factor linear mixed models.
Fisher Scoring for crossed factor linear mixed models.
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DOI：
10.1007/s11222-021-10026-6
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发表时间：
2021
影响因子：
2.2
通讯作者：
Nichols TE
中科院分区：
文献类型：
--
作者：
Maullin-Sapey T;Nichols TE
纵向、异构或不平衡聚类数据的分析对于广泛的应用是至关重要的。线性混合模型（LMM）是线性模型的一种流行和灵活的扩展，专门为这些目的而设计。从历史上看，LMM上发表的大部分材料涉及流行的数值优化算法的应用，如牛顿-拉夫森，费舍尔评分和期望最大化到单因子LMM（即LMM只包含一个“因子”，观察结果被分组）。然而，近年来，LMM文献的重点已经转向更复杂，多因子设计的估计和推理方法的发展。在本文中，我们提出并推导出新的表达式的经典用于单因素LMM参数估计，Fisher评分，多个，交叉因子设计的算法的扩展。通过模拟和真实的数据的例子，我们比较了五个变种的Fisher Scoring算法彼此之间，以及对基线建立的R包lme 4，并找到证据的正确性和强大的计算效率的五个建议的方法。此外，我们提供了一种新的方法LMM Satterthwaite自由度估计的基础上的分析结果，这不需要迭代梯度估计。通过仿真，我们发现，这种方法产生的估计具有较低的偏差和较低的方差比现有的方法。在线版补充材料可通过10.1007/s11222-021-10026-6获得。
The analysis of longitudinal, heterogeneous or unbalanced clustered data is of primary importance to a wide range of applications. The linear mixed model (LMM) is a popular and flexible extension of the linear model specifically designed for such purposes. Historically, a large proportion of material published on the LMM concerns the application of popular numerical optimization algorithms, such as Newton–Raphson, Fisher Scoring and expectation maximization to single-factor LMMs (i.e. LMMs that only contain one “factor” by which observations are grouped). However, in recent years, the focus of the LMM literature has moved towards the development of estimation and inference methods for more complex, multi-factored designs. In this paper, we present and derive new expressions for the extension of an algorithm classically used for single-factor LMM parameter estimation, Fisher Scoring, to multiple, crossed-factor designs. Through simulation and real data examples, we compare five variants of the Fisher Scoring algorithm with one another, as well as against a baseline established by the R package lme4, and find evidence of correctness and strong computational efficiency for four of the five proposed approaches. Additionally, we provide a new method for LMM Satterthwaite degrees of freedom estimation based on analytical results, which does not require iterative gradient estimation. Via simulation, we find that this approach produces estimates with both lower bias and lower variance than the existing methods. The online version supplementary material available at 10.1007/s11222-021-10026-6.
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影响因子：
1.4
作者：
POWELL, MJD
通讯作者：
POWELL, MJD
影响因子：
3.7
作者：
DEMPSTER, AP;RUBIN, DB;TSUTAKAWA, RK
通讯作者：
TSUTAKAWA, RK
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
DOI：
10.2307/3002019
发表时间：
1946-01-01
期刊：
BIOMETRICS BULLETIN
影响因子：
--
作者：
SATTERTHWAITE, FE
通讯作者：
SATTERTHWAITE, FE
影响因子：
1.9
作者：
LAIRD, NM;WARE, JH
通讯作者：
WARE, JH