Historically, time series forecasts of economic variables have used only a handful of
predictor variables, while forecasts based on a large number of predictors have been the …

In recent work, Wang et al introduced the “Sum of Single Effects”(SuSiE) model, and showed
that it provides a simple and efficient approach to fine-mapping genetic variants from …

We critically review and compare epidemiological designs and statistical approaches to
estimate associations between air pollution and health. More specifically, we aim to address …

We introduce a simple new approach to variable selection in linear regression, with a
particular focus on quantifying uncertainty in which variables should be selected. The …

An important problem in econometrics and marketing is to infer the causal impact that a
designed market intervention has exerted on an outcome metric over time. This paper …

Both linear mixed models (LMMs) and sparse regression models are widely used in
genetics applications, including, recently, polygenic modeling in genome-wide association …

As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses
has been frequently interpreted in the light of their genetic histories. Unfortunately, inference …

This article describes a system for short term forecasting based on an ensemble prediction
that averages over different combinations of predictors. The system combines a structural …

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive
manual for the applied researcher who wants to perform data analysis using linear and …

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical
analyses from a Bayesian perspective. Using examples largely from the authors' own …