Inyoung Kim

• STAT5404: Nonparametric statistics (Spring 2013)
• STAT5044: Regression and ANOVA (Fall 2012)
• STAT6474: Advanced Topics Bayesian Statistics (Spring 2012)
• STAT6514: Advanced Topics in Regression (Spring 2012)

• Semiparametric/Nonparametric modeling using regression/smoothing splines
• Nonlinear/linear mixed effect modeling
• Bayesian modeling and computation
• Biostatistics (Biology, Environmental Health, Epidemiology and Toxicology, Survival analysis)
• Bioinformatics (Functional Genomics, System Biology, Proteomics)

A complete list of publications can be found from my website (https://sites.google.com/vt.edu/inyoungk/home)

• Kim+, T., Kim*, I, and Lee, K. (2024), Weighted Conditional Network Testing for Multiple High-Dimensional Correlated Data Sets, Statistica Sinica, to appear
• Sun+, P, Kim*, I. and Lee, K (2024), Probabilistic weighted Dirichlet process mixture with an application to stochastic volatility model. The Canadian Journal of  Statistics, to appear
• Lin+, J. and Kim*, I. (2024), Gaussian Process Selections in Semiparametric Multi-Kernel Machine Regression for Multi-Pathway Analysis, Statistical Analysis and Data Mining: The ASA Data Science Journal, 17, e11699.
• Gao+, W, Kim*, I, Nam, W.,  Ren, X., Zhou, W., and Agah, M. (2023), Nonparametric Bayesian Functional Clustering with Applications to Racial Disparities in  Breast Cancer, Statistical Analysis and Data Mining: The ASA Data Science Journal, e11657
• Kim+, BJ and Kim*, I (2023). Joint Semiparametric Kernel Network Regression. Statistics In Medicine, 42, 5247-5265.
• Kim, I., Kim, T., Lee, K, and Yoon, J. (2022), New approach and analysis of the generalized constant elasticity of variance model.   Applied Stochastic Models in Business and Industry,  39, 114-155.
• Hwangbo, S., Lee, S, Lee, S, Hwang, H., Kim, I, and Park, T. (2022), Kernel-based hierarchical structural component models for pathway analysis, Bioinformatics, 38, 3078-3086. 
• AlBahar+, A., Kim, I., Yue, X. (2021), A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to image Detection in Manufacturing Systems, IEEE Transactions on Automation Science and Engineering, 19, 3222-3233.
• Zhang+, L. and Kim*, I. (2021), Finite mixtures of semiparametric Bayesian survival kernel machine regression: Application to breast cancer gene pathway subgroup analysis. Journal of the Royal Statistical Society: Series C, 70, 251-269.
• Sun+, P, Kim*, I. and Lee, K (2020), Flexible weighted Dirichlet process mixture modelling and evaluation to address the problem of forecasting return distribution. Journal of Nonparametric Statistics, 32, 989-1014
• L. Shan+, L. Cheng+, and I. Kim* (2020), Joint estimation of the two-level Gaussian graphical models across multiple classes. Journal of Computational and Graphical Statistics, 29, 562-579.
• Kim+, BJ and Kim*, I (2019), Flexible omnibus test in 1:M matched case-crossover study with measurement error in covariates. Statistical Methods in Medical Research, 29, 3019-3031. (ENAR outstanding student paper)
• I. Lobach, I. Kim, A. Alekseyenko, S. Lobach, and L Zhang (2019), A simple approximation to bias in the genetic effect estimates when multiple disease states share a clinical diagnosis. Genetic Epidemiology, 43, 522-531.
• Y. Xu+, I. Kim*, and R. J. Carroll (2019), A hybrid omnibus test for generalized semiparametric single-index models with high dimensional covariate sets. Biometrics, 75, 757-767.
• H. Mahmound+ and I. Kim* (2019), Semiparametric Spatial Mixed Effects Single Index Models. Computational Statistics and Data Analysis, 136, 108-122.
• L. Zhang+ and I. Kim*(2018), Semiparametric Bayesian Kernel Machine Survival model for Evaluating Pathway effects. Statistical Methods in Medical Research, 28, 3301-3317.
• J. Chen+, G. Terrell, I. Kim*, M. Deviglus (2018). Proportional Odds Model with Log-concave Density Estimation. Statistica Sinica, 30, 877-901.
• L. Shan+, and I. Kim* (2017). Joint Estimation of Multiple Gaussian Graphical Models across Unbalanced Classes. Computational Statistics and Data Analysis, 121, 89-103.
• Z. Fang+, I. Kim*, and J Jung (2017). Semiparametric Kernel-based regression for Evaluating interaction between Pathway effect and covariate. Journal of Agricultural, Biological, and Environmental Statistics, 23, 129-152.
• F. Guo**, I. Kim**, S. Klauer (2017). Semiparametric Bayesian models for evaluating time-variant driving risk factors using naturalistic driving data and case-crossover approach. Statistics in Medicine, 30, 160-174. 
• P. Sun+, I. Kim* (2017). Dual-Semiparametric Regression using Weighted Dirichlet Process Mixture, Computational Statistics and Data Analysis, 30, 160-174.
• L. Cheng+, L. Shan+, and I. Kim* (2017). Multilevel Gaussian Graphical Model, Journal of Statistical Planning and Inference, 190, 1-14.
• E. Park, I. Kim, S. Tan, and C. Spiegelman (2017). Bayesian spatial multivariate receptor modeling for multi-site multi-pollutant data. Technometrics, 60, 306-318.
• A. Ortega Villa+ and I. Kim* (2016). Semiparametric time varying coefficient model for matched case-crossover studies. Statistics in Medicine, 36, 998-1013.
• H. Mahmound+, I. Kim*, and H. Kim (2016). Semiparametric Single Index Multi Change points Model. Environmetrics, 27, 494-506.
• L. Cheng+, I. Kim*, and H. Pang (2016). Bayesian Semiparametric Regression for Pahtway Based Analysis with Zero-Inflated Clinical Outcomes. Journal of Agricultural, Biological, and Environmental Statistics, 21, 641-662.
• Z. Fang+, I. Kim* (2016). Flexible variable selection for recovering the sparsity in nonadditive multivariate nonparametric model. Biometrics, 72, 1155-1163. Best Biometrics Paper Award of the Year
• Z. Fang+ and I. Kim* (2015). Bayesian Ising Graphical Model for Variable Selection. Journal of Computational and Graphical Statistics, 25, 589-605.
• J. Chen+, I. Kim*, G. Terrell, L. Liu, and G. Toth (2014). Generalized partial linear single-index mixed model for repeated measures data. Journal of Nonparametric Statistics, 26, 291-302
• I. Kim*, H. Pang, and H. Zhao (2013). Statistical properties of semiparametric methods for evaluating pathway effects. Journal of Statistical Planning and Inference, 142, 745-763
• A. Maiti, I. Kim, and P. Schaumont(2012). A Robust physical unclonable function with enhanced challenge-response set. IEEE Transaction on Information Forensics and Security, 7, 333-345.
• C. Park, I. Kim*, and Y. Lee (2012). Error Variance Estimation in Nonparametric Regression under Lipschitz Condition and Small Sample Size. Journal of Statistical Planning and Inference, 142, 2369-2385
• I. Kim, H. Pang, and H. Zhao (2012). Bayesian Semiparametric Regression Models for Evaluating Pathway Effects on Clinical Continuous and Binary Outcomes. Statistics in Medicine, 15, 1633-1651
• I. Kim, Cheong, H. K., and H. Kim (2011). Semiparametric regression models for detecting effect modification in matched case-crossover Studies, Statistics in Medicine, 96, 1458-1468.
• I. Kim, N. D. Cohen, A. Roussell, and N. Wang (2010). A two-component nonlinear mixed effects model for longitudinal data, with application to gastric emptying studies, Statistics in Medicine, 29, 1839-1859.
• I. Kim, Y. Liu and H. Zhao (2007). Bayesian methods for predicting interacting protein pairs using domain information, Biometrics, 63, 824-833.    Biometrics Showcase Paper of the Year
• B. Kim, I. Kim, S. Lee, S. Kim, S. Rha and H. Chung (2005). Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer, Bioinformatics, 21 (4), 517-528.
• I. Kim and N. Cohen (2004). Semiparametric and nonparametric modeling of effect modification in matched studies, Computational Statistics and Data Analysis, 46, 631-643.
• I. Kim, N. Cohen and R. J. Carroll (2003). Semiparametric regression splines in matched case-control studies, Biometrics, 59, 1158-1169.
• I. Kim, N. Cohen and R. J. Carroll (2002). Effect heterogeneity by a matching covariate in matched case-control studies: A method for graphs-based representation, American Journal of Epidemiology, 156, 463-470.