91 citations · 131 across the 8 of their papers we have counts for
8 papers
Valid predictions of random quantities in linear mixed models
Nicholas Syring, Fernando Miguez, Jarad Niemi
In applications of linear mixed-effects models, experimenters often desire uncertainty quantification for random quantities, like predicted treatment effects for unobserved individ…
Robust posterior inference for Youden's index cutoff
Nicholas Syring
Youden's index cutoff is a classifier mapping a patient's diagnostic test outcome and available covariate information to a diagnostic category. Typically the cutoff is estimated in…
Stochastic optimization for numerical evaluation of imprecise probabilities
Nicholas Syring, Ryan Martin
In applications of imprecise probability, analysts must compute lower (or upper) expectations, defined as the infimum of an expectation over a set of parameter values. Monte Carlo…
Gibbs posterior concentration rates under sub-exponential type losses
Nicholas Syring, Ryan Martin
Bayesian posterior distributions are widely used for inference, but their dependence on a statistical model creates some challenges. In particular, there may be lots of nuisance pa…
BayesBD: An R Package for Bayesian Inference on Image Boundaries
Nicholas Syring, Meng Li
We present the BayesBD package providing Bayesian inference for boundaries of noisy images. The BayesBD package implements flexible Gaussian process priors indexed by the circle to…
Robust and rate-optimal Gibbs posterior inference on the boundary of a noisy image
Nicholas Syring, Ryan Martin
Detection of an image boundary when the pixel intensities are measured with noise is an important problem in image segmentation, with numerous applications in medical imaging and e…
Calibrating general posterior credible regions
Nicholas Syring, Ryan Martin
An advantage of methods that base inference on a posterior distribution is that credible regions are readily obtained. Except in well-specified situations, however, there is no gua…
Simulating from a gamma distribution with small shape parameter
Chuanhai Liu, Ryan Martin, Nick Syring
Simulating from a gamma distribution with small shape parameter is a challenging problem. Towards an efficient method, we obtain a limiting distribution for a suitably normalized g…