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Horseshoe Priors for Spatial Small Area Estimation: Regular Variation, Tail Robustness, and Deep Learning
Dhiman Bhadra, Nicholas Polson
Small area estimation borrows strength across domains to repair the poor precision of direct survey estimators. Two philosophies dominate the area-level literature. The first, desc…
Generative Causal Inference
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
Generative Bayesian Computation (GBC) methods are developed for Casual Inference. Generative methods are simulation-based methods that use a large training dataset to represent pos…
Deep Partial Least Squares for Instrumental Variable Regression
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to a flexible deep neural net…
Inverse Probability Weighting: from Survey Sampling to Evidence Estimation
Jyotishka Datta, Nicholas Polson
We consider the class of inverse probability weight (IPW) estimators, including the popular Horvitz-Thompson and Hajek estimators used routinely in survey sampling, causal inferenc…
Deep Learning Partial Least Squares
Nicholas Polson, Vadim Sokolov, Jianeng Xu
High dimensional data reduction techniques are provided by using partial least squares within deep learning. Our framework provides a nonlinear extension of PLS together with a dis…
Bayesian Inference for Gamma Models
Jingyu He, Nicholas Polson, Jianeng Xu
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. Our methodology applies to many situations in stati…