Published and Accepted papers

1. Lim, J., Yu, D. and Pyun, K. S. (2011). Hyper-parameter in hidden Markov random field. The Korean Journal of Applied Statistics, 24(1), 177–183.

2. Yu, D., Lim, J., Liang, F., Kim, K. and Kim, B. S. (2012). Permutation test for incomplete paired data with application to cDNA microarray data. Computational Statistics and Data Analysis, 56(3), 510–521.

3. Lim, J. Kim, J., Kim, S., Yu, D., Kim, K., and Kim, B. S. (2012). Detection of differentially expressed gene sets in a partially paired microarray data set. Statistical Applications in Genetics and Molecular Biology, 11(3), Article 5.

4. Lim, J., Lee, K., Yu, D., Liu, H., and Sherman, M. (2012). Parameter estimation in the spatial auto-logistic model with working independent subblocks. Computational Statistics and Data Analysis, 56(12), 4421–4432.

5. Yu, D. and Lim, J. (2013). Introduction to general purpose GPU computing. Journal of the Korean Data & Information Science Society, 24(5), 1043–1061.

6. Yu, D., Kim, M., Xiao, G. and Hwang, T. H. (2013). Review of biological network data and its applications. Genomics & Informatics, 11(4), 200–210.

7. Lee, S. H., Yu, D., Bachman, A. H., Lim, J. and Ardekani, B. A. (2014). Application of fused lasso logistic regression to the study of corpus callosum thickness in early Alzheimer’s disease. Journal of Neuroscience Methods, 221, 78–84.

8. Won, J., Lim, J., Yu, D., Kim, B. S. and Kim, K. (2014). Monotone false discovery rate. Statistics & Probability Letters, 87, 86–93.

9. Ng, C. T., Lim, J., Lee, K. E., Yu, D. and Choi, S. (2014). A fast algorithm to sample the number of vertexes and the area of the random convex hull on the unit square. Computational Statistics, 29, 1187–1205. doi:10.1007/s00180-014-0486-1.

10. Yu, D., Lee, S. J., Lee, W. J., Kim, S., Lim, J. and Kwon, S. W. (2015). Classification of spectral data using fused lasso logistic regression. Chemometrics and Intelligent Laboratory Systems, 142, 70–77.

11. Yu, D., Won, J., Lee, T., Lim, J. and Yoon, S. (2015). High-dimensional fused lasso regression using majorization-minimization and parallel processing. Journal of Computational and Graphical Statistics, 24(1), 121–153.

12. Yu, D. and Kim, B. (2015). A study on tuning parameter selection for MDPDE. Journal of the Korean Data & Information Science Society, 26(3), 549–559.

13. Jang, W., Lim, J., Lazar, N. A., Loh, J. M. and Yu, D. (2015). Some Properties of Generalized Fused Lasso and Its Applications to High Dimensional Data. Journal of the Korean Statistical Society, 44(3), 352–365.

14. Yu, D., Son, W., Lim, J. and Xiao, G. (2015). Statistical completion of partially identified graph with application to estimation of gene regulatory network. Biostatistics, 16(4), 670–685.

15. Park, S., Kim, S., Yu, D., Pena-Llopis, S., Gao, J., Park, J. S., Chen, B., Norris, J., Wang, X., Chen, M., Kim, M., Yong, J., Wardak, Z.,  Choe, K., Story, M., Starr, T., Cheong, J.-H., and Hwang, T. H. (2016). An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types. Bioinformatics, 32(11), 1643–1651.

16. Lee, S. H., Bachman, A. H., Yu, D., Lim, J., Ardekani, B. A., and for the Alzheimer’s Disease Neuroimaging Initiative. (2016). Predicting progression from mild cognitive impairment to Alzheimer’s disease using longitudinal callosal atrophy. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2, 68–74.

17. Kim, B. and Yu, D. (2016). BCDR algorithm for network estimation based on pseudo-likelihood with parallelization using GPU. Journal of the Korean Data & Information Science Society, 27(2), 381–394.

18. Yu, D. (2016). A study on bias effect of LASSO regression for model selection criteria. The Korean Journal of Applied Statistics, 29(4), 643–656.

19. Park, D., Yu, D., Kim, T. Y. (2016). Toward understanding the evolution of pathogens: complex system-based hierarchical genomic inference (CS-HGI). Quantitative Bio-Science, 35(1), 1–6.

20. Son, W., Lim, J., and Yu, D.  (2016). Detection of Multiple Change Points Using Penalized Least Square Methods: A Comparative Study Between L0 and L1 Penalty. The Korean Journal of Applied Statistics, 29(6), 1147–1154.

21.  Kim, B., Yu, D., and Won, J. (2018). Comparative study of computational algorithms for the Lasso with high-dimensional, highly correlated data. To appear in Applied Intelligence.

22. Yu, D., Lim, J., Wang, X., Liang, F. and Xiao, G. (2017). Enhanced Construction of Gene Regulatory Networks using Hub Gene Information. BMC Bioinformatics, 18:186.

23. Kim, S. C. and Yu, D. (2017). Identification of differentially expressed genes using tests based on multiple imputations. Quantitative Bio-Science, 36(1), 2331.

24. Kim, J., Yu, D., Lim, J., and Won, J. (2018). A peeling algorithm for multiple testing on a random field. To appear in Computational Statistics, 33(1), 503525.

25. Lee, J. H., Park, J., and Yu, D. (2018). Identifying factors associated with university hospitals' profitability based on fused lasso regression. Journal of the Korean Data & Information Science Society29(1), 8396.

26.  Park, H., Kang, J., Heo, S., and Yu, D. (2018). Comparative study of prediction models for corporate bond rating. The Korean Journal of Applied Statistics, 33(3), 367–382.

27. Yu, D., Lee, S. H., Lim, J., Xiao, G., Craddock, R. C., and Biswal, B. B. (2018). Fused Lasso Regression for Identifying Differential Correlations in
Brain Connectome Graphs. To appear in Statistical Analysis and Data Mining.





Working Papers


• Ko, S., Yu, D., and Won, J.(2018). A Continuum of Optimal Primal-Dual Algorithms for Convex Composite Minimization Problems with Applications to Structured Sparsity. Submitted to Journal of Computational and Graphical Statistics.

Yu, D., Lim, J. and Xiao, G. (2018). Reconstruction of Gene Regulatory Network by Joint Precision Matrix Estimation Based on Sparse Direct Association of Gene Expression and Copy Number Variation. In preparation.

Yu, D. and Won, J. (2018). Block-sparse extension of pseudo likelihood approach in precision matrix estimation. In preparation.

• Choi, Y. and Yu, D. (2018). Parallel Coordinate Descent for Sparse Pseudo-likelihood Covariance Selection. In preparation.



Presentations

1. "Estimation of Gaussian graphical model with partially known graph information." 2nd International workshop of the ERCIM working group on computing & statistics at Limasol, Cyprus (Oct. 2009).

2. "Permutation test for incomplete paired data with application to cDNA microarray data." IMS-APRM (Poster) at Seoul, Korea (Jun. 2009); Joint meeting of Japan-Korea special conference of statistics and the 2nd Japan-Korea statistics conference of young researchers at Okayama, Japan (Feb. 2010).

3. "Estimation of piecewise constant function from correlated signals in an fMRI experiment." KSS-ICSP 2011 (Poster) at Busan, Korea (Jul. 2011).

4. "Estimation of the shape constrained partially linear model and the liquidity cost." International conference on advances in probability and statistics at Hongkong, China (Dec. 2011).

5. "MM-GPU algorithm for fused lasso regression.", 2012-Fall Korean statistical society meeting at Seoul, Korea (Nov. 2012).

6. "Statistical completion of partially identified graph with application of estimation to gene regulatory network.", 2014 Conference of Texas statisticians (Poster) at UT Dallas, USA (Mar. 2014); 2015-Spring Korean statistical society meeting at Cheongju, Korea (May 2015).

7. "Construction of gene regulatory network incorporating potential hub gene information.", 2014-Fall Korean Data & Information Science Society meeting at Daegu, Korea (Nov. 2014).

8. "Classification of spectral data using fused lasso logistic regression.", 2015-Spring Korean Data & Information Science Society meeting at Busan, Korea (May 2015); 2015-Fall Korean statistical society meeting at Yongin, Korea (Nov. 2015).

9. "BCDR algorithm for network estimation based on pseudo-likelihood with parallelization using GPU.", 2016-Spring Korean statistical society meeting at Daegu, Korea (May 2016).

10. "A study on bias effect of LASSO regression for model selection criteria.", 2016-Spring Korean Data & Information Science Society meeting at Gyeongsan, Korea (May 2016).

11. "Enhanced Construction of Gene Regulatory Networks using Hub Gene Information." The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting at Hong Kong, China (Jun. 2016); 2017-Spring Korean statistical society meeting at Seoul, Korea (May. 2017).

12. "Penalized regression method for finding differences in brain connectome graphs.'", 2016 Joint Statistical Meetings at Chicago, USA (Aug. 2016);

EcoSta 2017 at Hong Kong (Jun. 2017).


13. "Sparse partial correlation estimation with scaled lasso.'", Young Statistician's Meeting 2017 at Yangpyeong (Jun., 2017).


14. "Efficient coordinate descent algorithm for sparse precision matrix estimation via scaled lasso", 2018-Spring Korean statistical society meeting at Busan, Korea (May. 2017).




Grants
  • (2015–2016) "A study on bias effect of LASSO regression for model selection criteria", Bisa Research Grant of Keimyung University.
  • (2015–2018) "Inference of network structures based on penalized regression models", Basic Science Research Program through the National Research Foundation of Korea.
  • (2017-2018) "Block-sparse extension of pseudo likelihood approach in precision matrix estimation", Research Grant of Inha University.
  • (2018-2019) "A comparative study for model selection in Gaussian graphical model", Research Grant of Inha University.
  • (2018-2021) "Precision matrix estimation with parallel computation using GPU", Basic Science Research Program through the National Research Foundation of Korea.