(* denotes advisee co-author at SNU/UMBC)
Online FDR Controlling procedures for statistical SIS Model and its application to COVID19 data
Hwang, S.* and Park, J.
Submitted.
Combining dependent p-values with transformation using empirical distribution of correlated variables
Kim, J. and Park, J.
Submitted.
Semiparametric empirical Bayes method for Normal mean estimation of the hierarchical Normal model with unknown heteroscedastic variances
Park, H. and Park, J.
Submitted.
General linear hypothesis testing of high-dimensional mean vectors based on random integration
Cao, M., Qiu, Y. and Park, J.
Submitted.
Online Anomaly Detection: Revisiting Storey’s Procedure with Adaptive Quantile Sketching
Hwang, S.* and Park, J.
Submitted.
A Group Sequential Procedure to Control FDR for Group Margin and Its Application to GWAS with Linkage Disequilibrium Scores
Kim, Y.*, Baek, S., Lee, D. and Park, J.
Submitted.
FDR controlling procedures with dimension reduction and their application to GWAS with
Linkage Disequilibrium score
Jung, D.*, Kim, Y. and Park, J.
Under Revision.
Classification of Multivariate Functional Data with an application to ADHD fMRI Data
Seong, Y.*, Gauran, I. I., Kim, H.*, Ombao, H. and Park, J.
Under Revision.
Two-Stage Multiple Test Procedures Controlling FDR with auxiliary variable and their Application to Set4Δ Mutant Data
Hwang, S.*, Ramos, M.L., Park, D., Park, J., Lim, J. and Green, E.
Biometrical Journal.
Combining p-values using heavy tailed distributions and their asymptotic results with applications to genomic data
Kim, J. and Park, J.
Statistica Sinica. [Article Link] [supplementary]
Model-assisted calibration estimation using Generalized entropy calibration in survey sampling
Kim, J., Kwon, Y., Qiu, Y. and Park, J.
Survey Methodology, 51(1), 197-215. [Article Link]
Variable Selection in Nonparametric Additive Models via Data Splitting
Kim, K.* and Park, J.
Statistical Analysis and Data Mining, 18(3), e70025. [Article Link]
High dimensional discriminant rules with shrinkage estimators of the covariance matrix and mean vector
Kim, J.*, Park, J. and Park, H.
Journal of Statistical Planning and Inference, 234, 106199. [Article Link]
A robust false discovery rate controlling procedure using empirical likelihood with a fast algorithm
Park, H. and Park, J.
Journal of Statistical Computation and Simulation, 94(5), 1097-1120. [Article Link]
Variable Selection Using Data Splitting and Projection for Principal Fitted Component Models in High Dimension
Baek, S., Park, H. and Park, J.
Computational Statistics and Data Analysis, 196, 107960. [Article Link] [supplementary]
An Exact and Near-Exact Distribution Approach to the Behrens–Fisher Problem
Hong, S.*, Coelho, A.C., Park, J.
Mathematics, 10(16), 2953. [Article Link]
Bayesian Local False Discovery Rate for sparse count data with application to the discovery of hotspots in protein domains
Gauran, I.I.*, Park, J., Rattsev, I., Peterson, T.A., Kann, M.G. and Park, D.
Annals of Applied Statistics, 16(3), 1459-1475. [Article Link]
Poisson mean vector estimation with Nonparametric Maximum Likelihood Estimation and Application to Protein Domain Data
Park, H.* and Park, J.
Electronic Journal of Statistics, 16(2), 3789-3835. [Article Link]
Efficient Integration of Aggregate Data and Individual Patient Data in One-Way Mixed Models
Agarwala, N.*, Park, J. and Roy, A.
Statistics in Medicine, 41(9), 1555-1572. [Article Link]
A Computationally Efficient Approach to Estimating Species Richness and Rarefaction Curve
Baek, S. and Park, J.
Computational Statistics, 37(4), 1919-1941. [Article Link]
High Dimensional Classification Based on Nonparametric Maximum Likelihood Estimation Under Unknown and Inhomogeneous Variances
Park, H.*, Baek, S. and Park, J.
Statistical Analysis and Data Mining, 15(2), 193-205. [Article Link]
High-dimensional linear discriminant analysis using nonparametric methods
Park, H.*, Baek, S. and Park, J.
Journal of Multivariate Analysis, 188, 104836. [Article Link]
Adaptive local false discovery rate procedures for highly spiky data and their application to protein Set4Δ data
Ramos, M.L., Park, D., Lim, J., Park, J., Tran, K., Garcia, E. and Green, E.
Biometrical Journal, 63(8), 1729-1744. [Article Link]
A High-Dimensional Classification Rule Using Sample Covariance Matrix Equipped With Adjusted Estimated Eigenvalues
Baek, S., Park, H.*, and Park, J.
Stat, 10(1), e358. [Article Link]
Revisit to Functional Data Analysis of Sleeping Energy Expenditure
Baek, S., Kim, Y.*, Park, J. and Lee, J.
Journal of Applied Statistics, 49(4), 988-1002. [Article Link]
Simultaneous test of mean vector and covariance matrix in high dimensional settings
Cao, M-X, Sun, P. and Park, J.
Journal of Statistical Planning and Inference, 212, 141-152. [Article Link]
Φ-admissibility of linear estimators of common mean parameter in general multivariate linear models under a balanced loss function
Cao, M.X., Park, J. and Shen, G.J.
Communication in Statistics: Theory and Method, 50, 4050–4065. [Article Link]
Horseshoe and Strawderman-Berger Estimators for Non-negative Normal Means
Neha, A.*, Park, J. and Roy, A.
Statistics and Applications, 18(2), 317–332. [Article Link]
An NMR Based Similarity Metric for Higher Order Structure Quality Assessment among U.S. Marketed Insulin Therapeutics
Wang, D., Park, J., Patil, S. M., Smith, C. J., Leazer Jr, J. L., Keire, D. A. and Chen, K.
Journal of Pharmaceutical Sciences, 109, 1519-1528.
A Spectral Measure for the Information Loss of Temporal Aggregation
Lee, B. and Park, J.
Journal of Statistical Theory and Practice, 14, 1-23. [Article Link]
Testing homogeneity of proportions from sparse binomial data with a large number of groups
Park, J.
Annals of the Institute of Statistical Mathematics, 71, 505-535. [Article Link]
Testing homogeneity of several normal population means based on interval hypotheses
Park, J. and Draganescu, A.
Communication in Statistics: Simulation and Computation, 50(12), 4114-4131. [Article Link]
Testing the homogeneity of risk differences with sparse count data
Park, J. and Gauran, I.I.*
Statistics, 53(6), 1306-1328. [Article Link]
A test for the k sample Behrens-Fisher problem in high dimensional data
Cao, M., Park, J. and He, D.
Journal of Statistical Planning and Inference, 201, 86-102. [Article Link]
Fixed support positive-definite modification of covariance matrix estimators via linear shrinkage
Choi, Y. Lim, J., Roy, A. and Park, J.
Journal of Multivariate Analysis, 171, 234-249. [Article Link]
Two-sample test for sparse high-dimensional multinomial distributions
Plunkett, A.* and Park, J.
TEST, 28, 804–826. [Article Link]
Testing equality of autocorrelation matrices at lag zero: Application to Resting State Networks
Ayyala, D.N.*, Roy, A., Park, J. and Rao, G.
Sankhya Ser. B, 80, 123-150. [Article Link]
Empirical Null Estimation using Discrete Mixture Distributions and its Application to Protein Domain Data
Gauran, I.I.*, Park, J., Lim, J., Park, D., Zylstra, J., Peterson, T., Kann, M. and Spouge, J.
Biometrics, 74, 458-471. [Article Link]
Simultaneous Estimation based on Empirical Likelihood and Nonparametric Maximum Likelihood Estimation
Park, J.
Computational Statistics and Data Analysis, 117, 19-31. [Article Link]
Mean vector testing for high-dimensional dependent observations
Ayyala, D.N.*, Park, J. and Roy, A.
Journal of Multivariate Analysis, 153, 136-155. [Article Link]
Oncodomains: A Protein Domain-Centric Framework for Analyzing Rare Variants in Tumor Samples
Peterson, T., Gauran, I.I.*, Park, J., Park, D. and Kann, M.
PLOS Computational Biology, 13(4), e1005428. [Article Link]
[Selected as a winner of 2018 PLOS Computational Biology Research Prize]
Chemometric Methods to Quantify 1D and 2D NM Spectral Differences among Similar Protein Therapeutics
K. Chen, J. Park, F. Li, S. M. Patil and D. A. Keire.
AAPS PhamSciTech, 19, 1011-1019. [Article Link]
Two sample testing of sparse high dimensional binary data
Plunkett, A.* and Park, J.
Communication in Statistics Theory and Methods, 46, 11181-11193. [Article Link]
Tolerance Limit and Ridge Regression in the presence of Mulicollinearity and High Dimension
Park, J.
Statistics and Probability Letters, 121, 128-135. [Article Link]