A hybrid model that estimates spatial dependence by maximum likelihood and then feeds spatially adjusted functional and scalar inputs into a deep neural network predicts spatially dependent outcomes with lower test error than linear spatial and non-spatial deep learning baselines.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Spatial Functional Deep Neural Network Model: A New Prediction Algorithm
A hybrid model that estimates spatial dependence by maximum likelihood and then feeds spatially adjusted functional and scalar inputs into a deep neural network predicts spatially dependent outcomes with lower test error than linear spatial and non-spatial deep learning baselines.