[论文] Implementing neural network mixed-effects models in Template Model Bui...

论文概要

研究领域: ML
作者: Nan Zheng, Hoi Yiu Cheung, Vibhu Sharma
发布时间: 2025-09-01
arXiv: 2509.00146

中文摘要

神经网络混合效应模型（NMM）通过结合人工神经网络的强表征和预测能力与混合效应建模捕捉复杂相关结构的能力而获得了关注。然而，现有的估计方法严重依赖目标函数和梯度的手动推导，这本质上迫使简化近似，并严重限制了NMM的复杂性和准确性。在这项工作中，我们介绍了使用模板模型构建器（TMB）实现NMM的通用框架。通过利用自动微分和拉普拉斯近似，TMB仅要求用户指定负联合对数似然和任何正则化项。该框架自动积分掉随机效应，并评估边缘目标函数及其精确梯度，消除了手动推导或临时近似的需要。我们在两个数值示例中展示了基于TMB的NMM的效率、灵活性和统计性能，包括单调NMM的应用。提供了可复现代码以促进更广泛的采用。

原文摘要

Neural network mixed-effects models (NMMs) have gained traction by combining the strong representation and predictive power of artificial neural networks with the capacity of mixed-effects modeling to capture complex correlation structures. However, existing estimation approaches rely heavily on manual derivations of objective functions and gradients, which inherently forces simplifying approximations and severely constrains the complexity and accuracy of NMMs. In this work, we introduce a general framework for implementing NMMs using Template Model Builder (TMB). By leveraging automatic differentiation and Laplace approximation, TMB requires users to specify only the negative joint log-likelihood and any regularization terms. The framework automatically integrates out random effects and e...

自动采集于 2026-09-02

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