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Found 6 papers, 2 papers with code
BART-SIMP: a novel framework for flexible spatial covariate modeling and prediction using Bayesian additive regression trees
Prediction is a classic challenge in spatial statistics and the inclusion of spatial covariates can greatly improve predictive performance when incorporated into a model with latent spatial effects.
Small Area Estimation with Random Forests and the LASSO
We consider random forests and LASSO methods for model-based small area estimation when the number of areas with sampled data is a small fraction of the total areas for which estimates are required.
A Penalized Poisson Likelihood Approach to High-Dimensional Semi-Parametric Inference for Doubly-Stochastic Point Processes
Doubly-stochastic point processes model the occurrence of events over a spatial domain as an inhomogeneous Poisson process conditioned on the realization of a random intensity function.
Space-time smoothing models for sub-national measles routine immunization coverage estimation with complex survey data
A key public health strategy for controling measles in such high-burden settings is to conduct supplementary immunization activities (SIAs) in the form of mass vaccination campaigns, in addition to delivering scheduled vaccination through routine immunization (RI) programs.
Applications Methodology
Spatio-Temporal Analysis of Surveillance Data
In this chapter, we consider space-time analysis of surveillance count data.
Applications Methodology
Efficient data augmentation for fitting stochastic epidemic models to prevalence data
We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school.
Computation Populations and Evolution
