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Influential Observations in Bayesian Regression Tree Models
Matthew T. Pratola, Edward I. George, Robert E. McCulloch
BCART (Bayesian Classification and Regression Trees) and BART (Bayesian Additive Regression Trees) are popular Bayesian regression models widely applicable in modern regression pro…
Clustering Areal Units at Multiple Levels of Resolution to Model Crime in Philadelphia
Cecilia Balocchi, Edward I. George, Shane T. Jensen
Estimation of the spatial heterogeneity in crime incidence across an entire city is an important step towards reducing crime and increasing our understanding of the physical and so…
Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO
Ray Bai, Veronika Rockova, Edward I. George
High-dimensional data sets have become ubiquitous in the past few decades, often with many more covariates than observations. In the frequentist setting, penalized likelihood metho…
Assumption Lean Regression
Richard A. Berk, Andreas Buja, Lawrence Brown +4
It is well known that models used in conventional regression analysis are commonly misspecified. A standard response is little more than a shrug. Data analysts invoke Box's maxim t…
Valid Post-selection Inference in Assumption-lean Linear Regression
Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja +2
Construction of valid statistical inference for estimators based on data-driven selection has received a lot of attention in the recent times. Berk et al. (2013) is possibly the fi…
Variance prior forms for high-dimensional Bayesian variable selection
Gemma E. Moran, Veronika Rockova, Edward I. George
Consider the problem of high dimensional variable selection for the Gaussian linear model when the unknown error variance is also of interest. In this paper, we show that the use o…