Joonsuk Park
Biographic Data
| ID | 8867448 |
|---|---|
| NAME | Joonsuk Park |
| GIVEN NAMES | Joonsuk |
| FAMILY NAME | Park |
| SIGNATURE | PARK J |
| AFFILIATIONS | The Ohio State University |
| ORCID | 0000-0003-0227-3283 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Which is Better for Individual Participant Data Meta-Analysis of Zero-Inflated Count Outcomes, One-Step or Two-Step Analysis? A Simulation Study
Conducting Bayesian-Classical Hybrid Power Analysis with R Package Hybridpower
There are several approaches to incorporating uncertainty in power analysis. We review these approaches and highlight the Bayesian-classical hybrid approach that has been implemented in the R package hybridpower. Calculating Bayesian-classical hybrid power circumvents the problem of local optimality in which calculated power is valid if and only if the specified inputs are perfectly correct. hybridpower can compute classical and Bayesian-classica…
HybridPower: An R Package for a Bayesian-Frequentist Hybrid Approach to Power Analysis
"HybridPower: An R Package for a Bayesian-Frequentist Hybrid Approach to Power Analysis." Multivariate Behavioral Research, 54(1), pp. 151–152
No prominent works on this page.
HybridPower: An R Package for a Bayesian-Frequentist Hybrid Approach to Power Analysis
"HybridPower: An R Package for a Bayesian-Frequentist Hybrid Approach to Power Analysis." Multivariate Behavioral Research, 54(1), pp. 151–152
Which is Better for Individual Participant Data Meta-Analysis of Zero-Inflated Count Outcomes, One-Step or Two-Step Analysis? A Simulation Study
Conducting Bayesian-Classical Hybrid Power Analysis with R Package Hybridpower
There are several approaches to incorporating uncertainty in power analysis. We review these approaches and highlight the Bayesian-classical hybrid approach that has been implemented in the R package hybridpower. Calculating Bayesian-classical hybrid power circumvents the problem of local optimality in which calculated power is valid if and only if the specified inputs are perfectly correct. hybridpower can compute classical and Bayesian-classica…
Algorithm (2 works) · Artificial Intelligence (2 works) · Artificial Intelligence (2 works) · Bayesian probability (2 works) · Computer Science (2 works) · Mathematics (2 works) · Advanced Multi-Objective Optimization Algorithms (1 works) · Bayesian inference (1 works) · Bayesian statistics (1 works) · Econometrics (1 works)