Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects.
Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects.
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DOI：
10.1093/biomtc/ujae016
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发表时间：
2023-05
期刊：
影响因子：
1.9
通讯作者：
H. Heiling;N. Rashid;Quefeng Li;X. Peng;Jen Jen Yeh-Jen;Joseph G. Ibrahim
中科院分区：
文献类型：
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作者：
H. Heiling;N. Rashid;Quefeng Li;X. Peng;Jen Jen Yeh-Jen;Joseph G. Ibrahim
现代的生物医学数据集越来越高维，并且表现出复杂的相关结构。长期以来，已使用广义线性混合模型（GLMM）来解释此类依赖性。但是，在高维度中，固定和随机效应的适当规范越来越困难，计算复杂性随机效应的尺寸增加而增长。我们使用随机效应的因子模型分解对GLMM进行了新的重新印象，从而可以通过将潜在空间从大量随机效应减少到较小的潜在因素来实现高维度的可扩展计算。我们还将先前的工作扩展到使用修改的蒙特卡洛期望条件最小化算法估算模型参数，从而使我们能够同时对固定和随机效应执行变量选择。我们通过模拟表明，通过此因子模型分解，我们的方法比可比较的方法更快地拟合了高维惩罚的GLMM，并且更容易地扩展到现有方法中以前未见的较大维度。
Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMs in high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We show through simulation that through this factor model decomposition, our method can fit high-dimensional penalized GLMMs faster than comparable methods and more easily scale to larger dimensions not previously seen in existing approaches.