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. Rejoinder

  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.