Computationally efficient Bayesian unit-level random neural network modelling of survey data under informative sampling for small area estimation
Computationally efficient Bayesian unit-level random neural network modelling of survey data under informative sampling for small area estimation
复制标题
小区域估计信息抽样下调查数据的计算高效贝叶斯单元级随机神经网络建模
DOI：
10.1093/jrsssa/qnad033
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Holan, Scott H.
中科院分区：
文献类型：
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作者：
Parker, Paul A.;Holan, Scott H.
近年来，神经网络的话题引起了人们的极大兴趣。然而，这些方法的主要挑战之一是量化不确定性。随机权重模型的使用提供了一个潜在的解决方案。除了不确定性量化之外，这些模型在计算上非常高效，因为它们不需要通过随机梯度下降进行优化。我们展示了如何使用这种方法可以通过使用伪似然来解释调查数据的信息抽样。我们说明了这种方法的有效性，通过模拟和数据应用，涉及美国国家选举研究数据。
The topic of neural networks has seen a surge of interest in recent years. However, one of the main challenges with these approaches is quantification of uncertainty. The use of random weight models offer a potential solution. In addition to uncertainty quantification, these models are extremely computationally efficient as they do not require optimisation through stochastic gradient descent. We show how this approach can be used to account for informative sampling of survey data through the use of a pseudo-likelihood. We illustrate the effectiveness of this methodology through simulation and data application involving American National Election Studies data.