Jin-young Choi
Biographic Data
| ID | 5089019 |
|---|---|
| NAME | Jin-young Choi |
| GIVEN NAMES | Jin-young |
| FAMILY NAME | Choi |
| SIGNATURE | CHOI J |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Dynamical-statistical method for seasonal forecasting of wintertime PM10 concentration in South Korea using multi-model ensemble climate forecasts
Climate conditions and emissions are among the primary influences on seasonal variations in air quality. Consequently, skillful climate forecasts can greatly enhance the predictability of air quality seasonal forecasts. In this study, we propose a dynamical-statistical method for seasonal forecasting of particulate matter (PM 10 ) concentrations in South Korea in winter using climate forecasts from the Asian Pacific Climate Center (APCC) multi-mo…
Regression Discontinuity with Multiple Running Variables Allowing Partial Effects
In regression discontinuity (RD), a running variable (or “score”) crossing a cutoff determines a treatment that affects the mean-regression function. This paper generalizes this usual “one-score mean RD” in three ways: (i) considering multiple scores, (ii) allowing partial effects due to each score crossing its own cutoff, not just the full effect with all scores crossing all cutoffs, and (iii) accommodating quantile/mode regressions. This genera…
No prominent works on this page.
Regression Discontinuity with Multiple Running Variables Allowing Partial Effects
In regression discontinuity (RD), a running variable (or “score”) crossing a cutoff determines a treatment that affects the mean-regression function. This paper generalizes this usual “one-score mean RD” in three ways: (i) considering multiple scores, (ii) allowing partial effects due to each score crossing its own cutoff, not just the full effect with all scores crossing all cutoffs, and (iii) accommodating quantile/mode regressions. This genera…
Dynamical-statistical method for seasonal forecasting of wintertime PM10 concentration in South Korea using multi-model ensemble climate forecasts
Climate conditions and emissions are among the primary influences on seasonal variations in air quality. Consequently, skillful climate forecasts can greatly enhance the predictability of air quality seasonal forecasts. In this study, we propose a dynamical-statistical method for seasonal forecasting of particulate matter (PM 10 ) concentrations in South Korea in winter using climate forecasts from the Asian Pacific Climate Center (APCC) multi-mo…
Advanced Causal Inference Techniques (1 works) · Air Quality Monitoring and Forecasting (1 works) · Atmospheric chemistry and aerosols (1 works) · Climate change (1 works) · Climate model (1 works) · Climatology (1 works) · Correlation (1 works) · Cutoff (1 works) · Econometrics (1 works) · Ensemble forecasting (1 works)