(* denotes advisee co-author at SNU/UMBC)
*Park, H. and Park, J. (2022+) A robust false discovery rate controlling procedure using empirical likelihood with a fast algorithm. Submitted.
*Kim, Y., Baek, S., Lee, D. and Park, J. (2022+) A Group Sequential Procedure to Control FDR for Group Margin and Its Application to GWAS with Linkage Disequilibrium Scores. Submitted.
*Park, H. and Park, J. Estimation of normal mean vector under unknown and unequal variances.
Park, A. and Vexler, A. Empirical likelihood via quadratic loss function.
Park, J., Park, D. and Spouge, J. False discovery rate and application to HIV data with BLOSSUM62.
Baek, S., *Park, H., and Park, J. Variable Selection by Mirror Statistics and Knockoff Statistics in Sufficient Dimension Reduction.
*Hong, S., Coelho, A.C., Park, J. (2022+) An Exact and Near-Exact Distribution Approach to the Behrens–Fisher Problem. Mathematics. Article Link
*Gauran, I.I., Park, J., Rattsev, I., Peterson, T.A., Kann, M.G. and Park, D. (2022+) Bayesian Local False Discovery Rate for sparse count data with application to the discovery of hotspots in protein domains. Annals of Applied Statistics. Article Link
*Park, H. and Park, J. (2022+) Poisson mean vector estimation with Nonparametric Maximum Likelihood Estimation and Application to Protein Domain Data. Electronic Journal of Statistics. Ariticle Link
*Agarwala, N., Park, J. and Roy, A. (2022+) Efficient Integration of Aggregate Data and Individual Patient Data in One-Way Mixed Models. Statistics in Medicine.Ariticle Link
Baek, S. and Park, J. (2022+) A Computationally Efficient Approach to Estimating Species Richness and Rarefaction Curve. Computational Statistics. Ariticle Link
*Park, H., Baek, S. and Park, J. (2022+) High Dimensional Classification Based on Nonparametric Maximum Likelihood Estimation Under Unknown and Inhomogeneous Variances. Statistical Analysis and Data Mining. Article Link
*Park, H., Baek, S. and Park, J. (2022) High-dimensional linear discriminant analysis using nonparametric methods
Journal of Multivariate Analysis. Article Link
Ramos, M.L., Park, D., Lim, J., Park, J., Tran, K., Garcia, E. and Green, E.
Adaptive local false discovery rate procedures for highly spiky data and their application to protein Set4Δ data
Biometrical Journal Article Link
Baek, S., *Park, H., and Park, J. A High-Dimensional Classification Rule Using Sample Covariance Matrix Equipped With Adjusted Estimated Eigenvalues
STAT Article Link
Baek, S., *Kim, Y., Park, J. and Lee, J. Revisit to Functional Data Analysis of Sleeping Energy Expenditure
Journal of Applied Statistics Article Link
Cao, M-X, Sun, P. and Park, J. Simultaneous test of mean vector and covariance matrix in high dimensional settings
Journal of Statistical Planning and Inference, 212, 141-152. Article Link
Cao, M.X., Park, J. and Shen, G.J. Φ-admissibil ity of linear estimators of common mean parameter in general multivariate linear models under a balanced loss function
Communication in Statistics: Theory and Method, 50, 4050–4065. Article Link
*Neha, A., Park, J. and Roy, A. (2021). Horseshoe and Strawderman-Berger Estimators for Non-negative Normal Means
Statistics and Applications, 18, No. 2 (New Series), 1–21. Article Link
Wang, D., Park, J., …., Chen, K. (2020) An NMR Based Similarity Metric for Higher Order Structure Quality Assessment among U.S. Marketed Insulin Therapeutics
Journal of Pharmaceutical Sciences, 109, 1519-1528.
Lee, B. and Park, J. (2020) A Spectral Measure for the Information Loss of Temporal Aggregation
Journal of Statistical Theory and Practice. Article Link
Park, J. (2019) Testing homogeneity of proportions from sparse binomial data with a large number of groups
Annals of the Institute of Statistical Mathematics, 71, 505-535. Article Link
Park, J. and Draganescu, A. (2019) Testing homogeneity of several normal population means based on interval hypotheses.
Communication in Statistics: Simulation and Computation. Article Link
Park, J. and *Gauran, I.I. (2019), Testing the homogeneity of risk differences with sparse count data.
Statistics:A Journal of Theoretical and Applied Statistics. Article Link
Cao, M., Park, J. and He, D. (2019) A test for the k sample Behrens-Fisher problem in high dimensional data.
Journal of Statistical Planning and Inference, 201, 86-102. Article Link
Choi, Y. Lim, J., Roy, A. and Park, J. (2019). Fixed support positive-definite modification of covariance matrix estimators via linear shrinkage
Journal of Multivariate Analysis, 171, 234-249. Article Link
*Plunkett, A. and Park, J. (2018). Two-sample test for sparse high-dimensional multinomial distributions
TEST , 28, 804–826. Article Link
*Ayyala, D.N., Roy, A., Park,J. and Rao, G. (2018)
Testing equality of autocorrelation matrices at lag zero: Application to Resting State Networks.
Sankhya Ser. B, 80, 123-150. Article Link
*Gauran, I.I., Park, J., Lim, J., Park, D., Zylstra, J., Peterson, T., Kann, M. and Spouge, J. (2018)
Empirical Null Estimation using Discrete Mixture Distributions and its Application to Protein Domain Data
Biometrics, 74, 458-471. Article Link
Park, J. (2018). Simultaneous Estimation based on Empirical Likelihood and Nonparametric Maximum Likelihood Estimation
Computational Statistics and Data Analysis, 117, 19-31. Article Link
*Ayyala, D.N., Park, J. and Roy, A. (2017) Mean vector testing for high-dimensional dependent observations
Journal of Multivariate Analysis, 153, 136-155. Article Link
Peterson, T., *Gauran, I.I., Park, J., Park, D. and Kann, M. (2017). Oncodomains: A Protein Domain-Centric Framework for Analyzing Rare Variants in Tumor Samples
PLOS Computational Biology. Article Link
Selected as a winner of 2018 PLOS Computational Biology Research Prize.
K. Chen, J. Park, F. Li, S. M. Patil and D. A. Keire (2017). Chemometric Methods to Quantify 1D and 2D NM Spectral Differences among Similar Protein Therapeutics
AAPS PhamSciTech. Article Link.
*Plunkett, A. and Park, J. (2017). Two sample testing of sparse high dimensional binary data
Communication in Statistics Theory and Methods, 46, 11181-11193. Article Link
Park, J. (2017). Tolerance Limit and Ridge Regression in the presence of Mulicollinearity and High Dimension
Statistics and Probability Letters, 121, 128-135. Article Link