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Research interests
Research interests
Causal inference
Copula
Semiparametrics
Spatial data analysis
Mijeong Kim is an Associate Professor in the Department of Statistics at the College of Natural Sciences, where she primarily teaches theoretical courses in statistics. Dr. Kim earned her PhD in Statistics from Texas A&M University. After completing her degree, she worked at Samsung Electronics Semiconductor for approximately two years, focusing on yield management and experimental design, before joining the Department of Statistics at Ewha Womans University.
Dr. Kim's main research areas include semiparametric methods, causal inference, and spatial data analysis. Her research has primarily focused on deriving more efficient estimators than those of parametric methods using semiparametric approaches. Recently, her research has shifted to applying causal inference techniques to spatial data analysis.
Selected publications
Selected publications
Kim, M. (2023). Appropriate use of parametric and nonparametric methods in estimating regression models with various shapes of errors. Stat, 12(1), e606.
Jeong, J., Kim, M., & Choi, J. (2023). Investigating the spatio-temporal variation of hepatitis A in Korea using a Bayesian model. Frontiers in Public Health, 10, 1085077.
Lee, W., Kim, M., & Ahn, J. Y. (2020). On structural properties of an asymmetric copula family and its statistical implication. Fuzzy Sets and Systems, 393, 126-142.
Kim, M., & Lin, S. (2020). Characterization of histone modification patterns and prediction of novel promoters using functional principal component analysis. PloS one, 15(5), e0233630.
Yang, J., & Kim, M. (2020). Independence test of a continuous random variable and a discrete random variable. Communications for Statistical Applications and Methods, 27(3), 285-299.
Kim, M., & Ma, Y. (2019). Semiparametric efficient estimators in heteroscedastic error models. Annals of the Institute of Statistical Mathematics, 71(1), 1-28.
Choe, H. M., Kim, M., & Lee, E. K. (2017). EMSaov: An R Package for the Analysis of Variance with the Expected Mean Squares and its Shiny Application. R J., 9(1), 252.
Ma, Y., Kim, M., & Genton, M. G. (2013). Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias. Journal of the American Statistical Association, 108(503), 1090-1104.
Genton, M. G., Kim, M., & Ma, Y. (2012). Semiparametric location estimation under non‐random sampling. Stat, 1(1), 1-11.
Kim, M., & Ma, Y. (2012). The efficiency of the second-order nonlinear least squares estimator and its extension. Annals of the Institute of Statistical Mathematics, 64(4), 751-764.
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