Jan Hannig

Professor

University of North Carolina at Chapel Hill

United States

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About

Jan Hannig is a Professor at the University of North Carolina at Chapel Hill, USA. His research focuses on neuroimaging data integration, generalized fiducial inference, and nonparametric statistics. Recent publications include topics such as data integration via analysis of subspaces, evidence measures for subpopulation differences, and methods for forensic identification challenges.

Articles (20)

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2025

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Rutgers University

UNITED STATES

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Jan Hannig

Research Interests

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About

Articles (20)

AutoGFI: Streamlined Generalized Fiducial Inference for Modern Inference Problems in Models with Additive Errors

Multifaceted Neuroimaging Data Integration via Analysis of Subspaces

A fiducial approach to the nonparametric deconvolution problem: The discrete case

Algorithm for Detection of Illegal Discounting in North Carolina Education Lottery

Data integration via analysis of subspaces (DIVAS)

Measure of Strength of Evidence for Visually Observed Differences between Subpopulations

Rejoinder on: Data integration via analysis of subspaces (DIVAS)

The EAS approach for graphical selection consistency in vector autoregression models

Technical Comment on “Policy impacts of statistical uncertainty and privacy”

Jackstraw inference for AJIVE data integration

A Unified Nonparametric Fiducial Approach to Interval-Censored Data

GENERALIZED FIDUCIAL FACTOR: AN ALTERNATIVE TO THE BAYES FACTOR FOR FORENSIC IDENTIFICATION OF SOURCE PROBLEMS

Model Validation of a Single Degree-of-Freedom Oscillator: A Case Study

A New String Edit Distance and Applications

Uncertainty quantification for honest regression trees

Testing for calibration discrepancy of reported likelihood ratios in forensic science

Generalized fiducial inference on the mean of zero-inflated Poisson and Poisson hurdle models

A note on optimal sampling strategy for structural variant detection using optical mapping

Covariance estimation via fiducial inference

Method G: Uncertainty Quantification for Distributed Data Problems Using Generalized Fiducial Inference

Collaborators (5)

Thomas C. M. Lee

Sujit Ghosh

Jonathan Williams

Edward Boone

Ruobin Gong

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