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EDUCATION

RESEARCH INTERESTS

Quantile regression; extreme value theory and applications; bioinformatics; nonparametric (semiparametric) regression; inference; variable selection; survival analysis; longitudinal data analysis; measurement error; missing data analysis

PROFESSIONAL EXPERINCE

AWARDS AND HONORS

GRANTS

TEACHING

UNDERGRADUATE RESEARCH SUPERVISION

DOCTORAL STUDENTS SUPERVISION

K12 TEACHER MENTORING

PHD COMMITTEE MEMBER

MS COMMITTEE CHAIR (NON-THESIS OPTION)

MS COMMITTEE MEMBER (THESIS OPTION)

MS COMMITTEE MEMBER (NON-THESIS OPTION)

EDITORIAL AND REVIEW SERVICE

UNIVERSITY COMMITTEE WORK

The George Washington University

North Carolina State University

PROFESSIONAL SERVICES

American Statistical Association (ASA)

Eastern North American Region (ENAR)

International Chinese Statistical Association (ICSA)

International Conference of Computational and Methdological Statistics MStatistics

International Conference of Robust Statistics

Other Services

INVITED SEMINARS

CONFERENCE TALKS

PUBLICATIONS

(Note: * denotes students/postdocs advised/co-advised, # denotes visiting students advised/co-advised)

Refereed Journal Articles Published and In-Press

  1. Hu, J., Zhang, L.# and Wang, H. (2016). Sequential model selection based segmentation to detect DNA copy number variation. Biometrics, 72, 815-826.

  2. Sun,Y., Wang, H. and Fuentes, M. (2016). Fused Lasso for spatial and temporal quantile function estimation. Technometrics, 58, 127-137.

  3. Wang, K.* and Wang, H. (2016). Optimally combined estimation for tail quantile regression. Statistica Sinica, 26, 295-311.

  4. Yang, Y., Wang, H. and He, X. (2015). Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood (Discussion Paper). International Statistical Review, to appear.

  5. Zhou, M.*, Wang, H. and Tang, Y. (2015). Sequential change point detection in linear quantile regression models. Statistics and Probability Letters, 100, 98–103.

  6. Zhang, L.#, Wang, H. and Zhu, Z. (2015). Composite change point estimation for bent line quantile regression. Annals of the Institute of Statistical Mathematics, DOI 10.1007/s10463-015-0538-5, 1–24.

  7. Tang, Y. and Wang, H. (2015). Penalized regression across multiple quantiles under random censoring. Journal of Multivariate Analysis, 141, 132-146.

  8. Pang, L.*, Lu, W. and Wang, H. (2015). Local Buckley-James estimator for the heteroscedastic accelerated failure time model. Statistica Sinica, 25, 863-877.

  9. Dupis, D., Sun, Y. and Wang, H. (2015). Detecting change-points in temperature extremes: the story for the U.S. northeast coast. Statistics and Its Interface, 8, 19-31

  10. Jang, W.* and Wang, H. (2015). A semiparametric Bayesian approach for quantile regression with clustered data. Computational Statistics and Data Analysis, 84, 99-115.

  11. Bernhardt, P. W.*, Zhang, D. and Wang, H. (2015). A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits. Computational Statistics and Data Analysis, 85, 37-53.

  12. Zhang, L.#, Wang, H. and Zhu, Z. (2014). Testing for change points due to a covariate threshold in regression quantiles. Statistica Sinica, 24, 1859-1877.

  13. Wang, H. and Wang, L. (2014). Quantile regression analysis of length-biased survival data. Stat, 3, 31-47.

  14. Jiang, L.*, Bondell, H. and Wang, H. (2014). Interquantile shrinkage and variable selection in quantile regression. Computational Statistics and Data Analysis, 69, 208-219.

  15. Bernhardt, P. W.*, Wang, H. and Zhang, D. (2014). Flexible modeling of survival data with covariates subject to detection limits. Computational Statistics and Data Analysis, 69, 81-91.

  16. Wang, H. and Li, D. (2013). Estimation of conditional high quantiles through power transformation. Journal of the American Statistical Association, 108, 1062-1074.

  17. Torres, P. A.*, Zhang, D. and Wang, H. (2013). Constructing conditional reference charts for grip strength measured with error. Topics in Applied Statistics—2012 Symposium of the International Chinese Statistical Association, 299-310, Springer, New York.

  18. Bernhardt, P. W.*, Wang, H. and Zhang, D. (2013). Multiple imputation for generalized linear models with censored covariates. Statistics in Biosciences, DOI 10.1007/s12561-013-9099-4.

  19. Jiang, L.*, Wang, H. and Bondell, H. (2013). Interquantile shrinkage in regression models. Journal of Computational and Graphical Statistics, 22, 970-986.

  20. Tang, Y., Song, X., Wang, H. and Zhu, Z. (2013). Variable selection in high-dimensional quantile varying coefficient models. Journal of Multivariate Analysis, 122, 115-132.

  21. Wang, H., Zhou, J., and Li, Y. (2013). Variable selection for censored quantile regression. Statistica Sinica, 23, 145-167.

  22. Tang, Y.#, Wang, H., and Zhu, Z. (2013). Variable selection in quantile varying coefficient models with longitudinal data. Computational Statistics and Data Analysis, 57, 435-449.

  23. Wang, H., Li, D. and He, X. (2012). Estimation of high conditional quantiles for heavy-tailed distributions. Journal of the American Statistical Association, 107, 1453-1464.

  24. Wang, H., Stefanski, L., and Zhu, Z. (2012). Corrected-loss estimation for quantile regression with covariate measurement error. Biometrika, 99, 405-421.
  25. Tang, Y.#, Wang, H., He, X., and Zhu, Z. (2012). Informative subset estimation for censored quantile regression. TEST, 21, 635-655.
  26. Wang, H. and Feng, X. (2012). Multiple imputation for \(M\)-regression with censored covariates. Journal of the American Statistical Association, 107, 194-204.
  27. Tang, Y.#, Wang, H., Zhu, Z., and Song, X. (2012). A unified variable selection approach for varying coefficient models. Statistica Sinica, 22, 601-628.
  28. Sun, Y., Wang, H., and Gilbert, P. B. (2012). Quantile regression for competing risks data with missing cause of failure. Statistica Sinica, 22, 703-728.
  29. Pang, L.*, Lu, W., and Wang, H. (2012). Variance estimation in censored quantile regression via induced smoothing. Computational Statistics and Data Analysis, 56, 785-796.
  30. Wang, H. and Zhu, Z. (2011). Empirical likelihood for quantile regression models with longitudinal data. Journal of Statistical Planning and Inference, 141, 1603-1615.
  31. Wang, H. and Hu, J. (2011). Identification of differential aberrations in multiple-sample array CGH studies. Biometrics, 67, 353-362.
  32. Bondell, H. D., Reich, B. J., and Wang, H. (2010). Non-crossing quantile regression curve estimation. Biometrika, 97, 825-838.
  33. Reich, B. J., Bondell, H. D., and Wang, H. (2010). Flexible Bayesian quantile regression for independent and clustered data. Biostatistics, 11, 337-352.
  34. Ayers, C. R., Moorman, C. E., Deperno, C. S., Yelverton, F. H., and Wang, H. (2010). Effects of mowing on anthraquinone for deterrence of Canada geese. Journal of Wildlife Management, 74, 1863-1868.
  35. Wang, H. and Zhou, X. (2010). Estimation of the retransformed conditional mean in health care cost studies. Biometrika, 97, 147-158.
  36. Wang, H. (2009). Inference on quantile regression for heteroscedastic mixed models. Statistica Sinica, 19, 1247-1261.
  37. Wang, H. and Fygenson, M. (2009). Inference for censored quantile regression models in longitudinal studies. Annals of Statistics, 37, 756-781.
  38. Wang, H. and Wang, L. (2009). Locally weighted censored quantile regression. Journal of the American Statistical Association, 104, 1117-1128. Here are some additional remarks for the paper.
  39. Wang, H., Zhu, Z., and Zhou, J. (2009). Quantile regression in partially linear varying coefficient models. Annals of Statistics, 37, 3841-3866.
  40. Thomas, R., Duke, S. E., Wang, H., Breen, T. E., Higgins, R. J., Linder, K. E., Ellis, P, Langford, C. F., Dickinon, P. J., Olby, N. J., and Breen, M. (2009). `Putting our heads together’-insights into genomic conservation between human and canine intracranial tumors. Journal of Neuro-Oncology, 94, 333-349.
  41. Thomas, R., Wang, H., Tsai, P. C., Langford, C. F., Fosmire, S. P., Jubala, C. M., Getzy, D. M., Cutter, G. R., Modiano, J. F., and Breen, M. (2009). Influence of genetic background on tumor karyotypes: evidence for breed-associated cytogenetic aberrations in canine appendicular osteosarcoma. Chromosome Research, 17, 365-377.
  42. Zhou, C., Wang, H., and Wang, Y. M. (2009). Efficient moments-based permutation tests. Neural Information Processing Systems (NIPS), pp. 2277-2285.
  43. Zhou, C., Hu, Y., Fu, Y., Wang, H., Huang, T. S., and Wang, Y. M. (2008). 3D face analysis for distinct features using statistical randomization. IEEE International Conference on Acoustics, Speech, and Digital Processing (ICASSP), 981-984.
  44. Wang, H. and He, X. (2008). An enhanced quantile approach for assessing differential gene expressions. Biometrics, 64, 449-457.
  45. Wang, H. and He, X. (2007). Detecting differential expressions in GeneChip microarray studies: a quantile approach. Journal of the American Statistical Association, 102, 104-112.
  46. Wang, H. and Huang, S. (2007). Mixture-model classification in DNA content analysis. Cytometry, 71A, 716-723.
  47. Wang, H., Huang, S., Shou, J., Wu, E.W., Onyia, J. E., Liao, B., and Li, S. (2006). Comparative analysis and integrative classification of NCI60 cell lines and primary tumors using gene expression profiling data. BMC Genomics, 7:166.
  48. Wang, H., He, X., Band, M., Wilson, C., and Liu, L. (2005). A study of inter-lab and inter-platform agreement of DNA microarray data. BMC Genomics, 6:71.
  49. Selvaraj, V., Bunick, D., Johnson, R. W., Wang, H., Liu, L., and Cooke, P. S. (2005). Gene expression profiling of 17\(\beta\)-Estradiol and genistein effects on mouse thymus. Toxicological Sciences, 87, 97-112.
  50. Zheng, Z., Wang, H., and Yan, M. (2001). The GDM model and survival estimation. Chinese Journal of Applied Probability and Statistics, 17, 213-216.

Book Chapters, Comments and Book Reviews

  1. Wang, H. (2016). Review of “Adaptive Design Theory and Implementation Using SAS and R (2nd ed.)”. The American Statistician, 69, 425-434.

  2. Wang, H. and Li, D. (2015). Estimation of Extreme Conditional Quantiles in Dey, D. and Yan, J. (Eds), Extreme Value Modeling and Risk Analysis: Methods and Applications (pp 319-337), Chapman and Hall/CRC.

  3. Barut, E. and Wang, H. (2015). Comments on ``An adaptive resampling test for detecting the presence of significant predictors" by I. McKeague and M. Qian. The Journal of American Statistical Association, 110, 1442-1445.