142 citations · 306 across the 18 of their papers we have counts for
7 papers · 1 filter
Bridging Bayesian, frequentist and fiducial (BFF) inferences using confidence distribution
Suzanne Thornton, Minge Xie
Bayesian, frequentist and fiducial (BFF) inferences are much more congruous than they have been perceived historically in the scientific community (cf., Reid and Cox 2015; Kass 201…
Nonparametric fusion learning: synthesize inferences from diverse sources using depth confidence distribution
Dungang Liu, Regina Y. Liu, Minge Xie
Fusion learning refers to synthesizing inferences from multiple sources or studies to provide more effective inference and prediction than from any individual source or study alone…
A Bias Correction Method in Meta-analysis of Randomized Clinical Trials with no Adjustments for Zero-inflated Outcomes
Zhengyang Zhou, Minge Xie, David Huh +1
Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive z…
Leveraging the Fisher randomization test using confidence distributions: inference, combination and fusion learning
Xiaokang Luo, Tirthankar Dasgupta, Minge Xie +1
The flexibility and wide applicability of the Fisher randomization test (FRT) makes it an attractive tool for assessment of causal effects of interventions from modern-day randomiz…
Homeostasis phenomenon in predictive inference when using a wrong learning model: a tale of random split of data into training and test sets
Min-ge Xie, Zheshi Zheng
This note uses a conformal prediction procedure to provide further support on several points discussed by Professor Efron (Efron, 2020) concerning prediction, estimation and IID as…
p-Value as the Strength of Evidence Measured by Confidence Distribution
Sifan Liu, Regina Liu, Min-ge Xie
The notion of p-value is a fundamental concept in statistical inference and has been widely used for reporting outcomes of hypothesis tests. However, p-value is often misinterprete…
Geometric Conditions for the Discrepant Posterior Phenomenon and Connections to Simpson's Paradox
Yang Chen, Ruobin Gong, Min-ge Xie
The discrepant posterior phenomenon (DPP) is a counter-intuitive phenomenon that can frequently occur in a Bayesian analysis of multivariate parameters. It refers to the phenomenon…