168 citations · 1.5k across the 73 of their papers we have counts for
5 papers · 1 filter
Valid uncertainty quantification about the model in a linear regression setting
Ryan Martin, Huiping Xu, Zuoyi Zhang +1
In scientific applications, there often are several competing models that could be fit to the observed data, so quantification of the model uncertainty is of fundamental importance…
Frameworks for prior-free posterior probabilistic inference
Chuanhai Liu, Ryan Martin
The development of statistical methods for valid and efficient probabilistic inference without prior distributions has a long history. Fisher's fiducial inference is perhaps the mo…
Exact prior-free probabilistic inference on the heritability coefficient in a linear mixed model
Qianshun Cheng, Xu Gao, Ryan Martin
Linear mixed-effect models with two variance components are often used when variability comes from two sources. In genetics applications, variation in observed traits can be attrib…
Empirical Bayes posterior concentration in sparse high-dimensional linear models
Ryan Martin, Raymond Mess, Stephen G. Walker
We propose a new empirical Bayes approach for inference in the normal linear model. The novelty is the use of data in the prior in two ways, for centering and regularizat…
Prior-free probabilistic prediction of future observations
Ryan Martin, Rama Lingham
Prediction of future observations is a fundamental problem in statistics. Here we present a general approach based on the recently developed inferential model (IM) framework. We em…