论文标题
计算有效的深贝叶斯单位单位级建模在信息性抽样下的小面积估计下的调查数据建模
Computationally Efficient Deep Bayesian Unit-Level Modeling of Survey Data under Informative Sampling for Small Area Estimation
论文作者
论文摘要
近年来，深度学习的话题引起了人们在统计领域内外的兴趣激增。与线性或广义线性模型相比，深层模型利用非线性和相互作用效应在许多情况下提供了出色的预测。但是，深层建模方法的主要挑战之一是量化不确定性。在这方面，使用随机重量模型（例如普通的“极限学习机”）提供了潜在的解决方案。除了不确定性量化外，这些模型在计算上非常有效，因为它们不需要通过随机梯度下降进行优化，这通常是为了深度学习而做的。我们展示了在深层模型中使用随机权重的使用如何适合基于可能性的框架，以允许对模型参数的不确定性定量和任何所需的估计。此外，我们展示了如何使用这种方法来说明通过使用伪样性的调查数据的信息采样。我们通过模拟以及涉及美国国家选举研究数据的实际调查数据应用来说明这种方法的有效性。
The topic of deep learning has seen a surge of interest in recent years both within and outside of the field of Statistics. Deep models leverage both nonlinearity and interaction effects to provide superior predictions in many cases when compared to linear or generalized linear models. However, one of the main challenges with deep modeling approaches is quantification of uncertainty. The use of random weight models, such as the popularized "Extreme Learning Machine," offer a potential solution in this regard. In addition to uncertainty quantification, these models are extremely computationally efficient as they do not require optimization through stochastic gradient descent, which is what is typically done for deep learning. We show how the use of random weights in a deep model can fit into a likelihood based framework to allow for uncertainty quantification of the model parameters and any desired estimates. Furthermore, 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 with a real survey data application involving American National Election Studies data.
