A Bayesian hierarchical model for joint meta-analysis of two diagnostic tests that models conditional dependence through study-specific log-odds ratios and accommodates studies without joint classification data or perfect gold standards.
2020.Statistical Rethinking: A Bayesian Course with Examples in R and Stan(2 ed.)
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 5verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
Simulations of predator-prey equations across 220,000 parameter sets show habituation, sensitization, and discrete number learning in recovery times, with strong asymmetry between response magnitude and recovery time.
Introduces the VET framework to categorize and critique polarized AI narratives including hype, doom, denial, and normalcy.
Proposes mCCDF plots to visualize ordinal regression results and communicate key takeaways from analyses of ordinal data like Likert scales.
UTAUT is suitable for studying individual barriers to GenAI use in software engineering when combined with Bayesian analysis, with three priorities for future research on construct refinement, operationalization, and statistical methods.
citing papers explorer
-
A joint meta-analysis framework for the accuracy of two diagnostic tests accounting for varying study designs
A Bayesian hierarchical model for joint meta-analysis of two diagnostic tests that models conditional dependence through study-specific log-odds ratios and accommodates studies without joint classification data or perfect gold standards.
-
Training Ecosystems: A Computational Approach to Uncovering Learning Behavior in Unconventional Contexts
Simulations of predator-prey equations across 220,000 parameter sets show habituation, sensitization, and discrete number learning in recovery times, with strong asymmetry between response magnitude and recovery time.
-
VET: A Framework for Analyzing AI Discourse
Introduces the VET framework to categorize and critique polarized AI narratives including hype, doom, denial, and normalcy.
-
Adapting CCDF Plots for Visualizing Ordinal Regression Results
Proposes mCCDF plots to visualize ordinal regression results and communicate key takeaways from analyses of ordinal data like Likert scales.
-
GenAI in Software Engineering: The Role of Technology Acceptance Models
UTAUT is suitable for studying individual barriers to GenAI use in software engineering when combined with Bayesian analysis, with three priorities for future research on construct refinement, operationalization, and statistical methods.