Implementing neural network mixed-effects models in Template Model Builder (TMB)
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-l...
| Authors | Nan Zheng, Hoi Yiu Cheung, Vibhu Sharma et al. |
|---|---|
| Primary category | stat.ML |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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