36 citations · 104 across the 18 of their papers we have counts for
14 papers · 1 filter
Robust distribution-free tests for the linear model
Torey Hilbert, Steven MacEachern, Yuan Zhang
Recently, there has been growing concern about heavy-tailed and skewed noise in biological data. We introduce RobustPALMRT, a flexible permutation framework for testing the associa…
Regularized Exponentially Tilted Empirical Likelihood for Bayesian Inference
Eunseop Kim, Steven N. MacEachern, Mario Peruggia
Bayesian inference with empirical likelihood faces a challenge as the posterior domain is a proper subset of the original parameter space due to the convex hull constraint. We prop…
A Process of Dependent Quantile Pyramids
Hyoin An, Steven N. MacEachern
Despite the practicality of quantile regression (QR), simultaneous estimation of multiple QR curves continues to be challenging. We address this problem by proposing a Bayesian non…
Superset model problem
Koji Miyawaki, Steven N. MacEachern
This paper focuses on the superset model problem that arises in the context of regression. To address this problem, we take the Bayesian approach to measure its uncertainty. An ill…
Familial inference: tests for hypotheses on a family of centres
Ryan Thompson, Catherine S. Forbes, Steven N. MacEachern +1
Statistical hypotheses are translations of scientific hypotheses into statements about one or more distributions, often concerning their centre. Tests that assess statistical hypot…
Empirical likelihood for the analysis of experimental designs
Eunseop Kim, Steven N. MacEachern, Mario Peruggia
Empirical likelihood enables a nonparametric, likelihood-driven style of inference without restrictive assumptions routinely made in parametric models. We develop a framework for a…
The Dependent Dirichlet Process and Related Models
Fernand A. Quintana, Peter Mueller, Alejandro Jara +1
Standard regression approaches assume that some finite number of the response distribution characteristics, such as location and scale, change as a (parametric or nonparametric) fu…
A regression approach to the two-dataset problem
Steven N. MacEachern, Koji Miyawaki
This paper considers the two-dataset problem, where data are collected from two potentially different populations sharing common aspects. This problem arises when data are collecte…
Economic variable selection
Steven N. MacEachern, Koji Miyawaki
Regression plays a key role in many research areas and its variable selection is a classic and major problem. This study emphasizes cost of predictors to be purchased for future us…
Bayesian Restricted Likelihood Methods: Conditioning on Insufficient Statistics in Bayesian Regression
John R. Lewis, Steven N. MacEachern, Yoonkyung Lee
Bayesian methods have proven themselves to be successful across a wide range of scientific problems and have many well-documented advantages over competing methods. However, these…
Marginally Interpretable Generalized Linear Mixed Models
Jeffrey J. Gory, Peter F. Craigmile, Steven N. MacEachern
Two popular approaches for relating correlated measurements of a non-Gaussian response variable to a set of predictors are to fit a marginal model using generalized estimating equa…
Bandwidth Selection for Kernel Density Estimation with a Markov Chain Monte Carlo Sample
Hang J. Kim, Steven N. MacEachern, Yoonsuh Jung
Markov chain Monte Carlo samplers produce dependent streams of variates drawn from the limiting distribution of the Markov chain. With this as motivation, we introduce novel univar…
Regularization of Case-Specific Parameters for Robustness and Efficiency
Yoonkyung Lee, Steven N. MacEachern, Yoonsuh Jung
Regularization methods allow one to handle a variety of inferential problems where there are more covariates than cases. This allows one to consider a potentially enormous number o…
Restricting exchangeable nonparametric distributions
Sinead Williamson, Zoubin Ghahramani, Steven N. MacEachern +1
Distributions over exchangeable matrices with infinitely many columns, such as the Indian buffet process, are useful in constructing nonparametric latent variable models. However,…