We develop a new methodology for spatial regression of aggregated outputs on multi-resolution covariates. Such problems often occur with spatial data, for example in crop yield prediction, where the output is spatially-aggregated over an area and the covariates may be observed at multiple resolutions. Building upon previous work on aggregated output regression, we propose a regression framework to synthesise the effects of the covariates at different resolutions on the output and provide uncertainty estimation. We show that, for a crop yield prediction problem, our approach is more scalable, via variational inference, than existing multi-resolution regression models. We also show that our framework yields good predictive performance, compared to existing multi-resolution crop yield models, whilst being able to provide estimation of the underlying spatial effects.

翻译：我们为多分辨率共变总产出的空间回归开发了新的方法。这些问题经常发生在空间数据中,例如作物产量预测中,其中产出在空间上对一个区域进行汇总,而共变数可在多个分辨率中观察到。根据以往关于总产出回归的工作,我们提出了一个回归框架,以综合不同分辨率的共变对产出的影响,并提供不确定性估计。我们表明,对于作物产量预测问题,我们的方法比现有的多分辨率回归模型更加可变。我们还表明,与现有的多分辨率作物产量模型相比,我们的框架具有良好的预测性能,同时能够提供对基本空间效应的估计。