101 citations · 294 across the 22 of their papers we have counts for
24 papers
Bayesian analysis of regression discontinuity designs with heterogeneous treatment effects
Kevin Tao, Y. Samuel Wang, David Ruppert
Regression Discontinuity Design (RDD) is a popular framework for estimating a causal effect in settings where treatment is assigned if an observed covariate exceeds a fixed thresho…
Maximizing Portfolio Predictability with Machine Learning
Michael Pinelis, David Ruppert
We construct the maximally predictable portfolio (MPP) of stocks using machine learning. Solving for the optimal constrained weights in the multi-asset MPP gives portfolios with a…
Bayesian Functional Principal Components Analysis via Variational Message Passing
Tui H. Nolan, Jeff Goldsmith, David Ruppert
Functional principal components analysis is a popular tool for inference on functional data. Standard approaches rely on an eigendecomposition of a smoothed covariance surface in o…
Bootstrap inference for quantile-based modal regression
Tao Zhang, Kengo Kato, David Ruppert
In this paper, we develop uniform inference methods for the conditional mode based on quantile regression. Specifically, we propose to estimate the conditional mode by minimizing t…
Machine Learning Portfolio Allocation
Michael Pinelis, David Ruppert
We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules…
Finite Sample Hypothesis Tests for Stacked Estimating Equations
Eli S. Kravitz, Raymond J. Carroll, David Ruppert
Suppose there are two unknown parameters, each parameter is the solution to an estimating equation, and the estimating equation of one parameter depends on the other parameter. The…