Department of Statistics and Actuarial Science
University of Waterloo
Ph.D. Department of Statistics, Iowa State University
M.S. Department of Statistics, Iowa State University
M.S. Department of Automation, Tsinghua University
B.E. Department of Automation, Tsinghua University
Tian, Z.*, Liang, K., and Li, P. (2020+), “A powerful procedure that controls the false discovery rate with directional information,” Biometrics, in press. paper
MacDonald, P.*, Wilson, N.*, Liang, K., and Qin, Y. (2020+), “Controlling the false discovery rate of grouped hypotheses,” in Modern Statistical Methods for Health Research, Springer, in press.
MacDonald, P.*, Liang, K., and Janssen, A. (2019), “Dynamic adaptive procedures that control the false discovery rate,” Electronic Journal of Statistics, 13, 3009–3024.
Liang, K., Du, C., You, H.*, and Nettleton, D. (2018), “A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets,” BMC Bioinformatics, 19.
Nie, Z.*, and Liang, K. (2017), “Adaptive filtering increases power to detect differentially expressed genes,” in New Advances in Statistics and Data Science, Springer, pp. 127–136.
Liang, K. (2016), “False discovery rate estimation for large-scale homogeneous discrete p-values,” Biometrics, 72, 639–648.
Liang, K., and Keleş, S. (2012), “Detecting differential binding of transcription factors with ChIP-seq,” Bioinformatics, 28, 121–122.
Liang, K., and Keleş, S. (2012), “Normalization of ChIP-seq data with control,” BMC Bioinformatics, 13, 199.
Liang, K., and Nettleton, D. (2012), “Adaptive and dynamic adaptive procedures for false discovery rate control and estimation,” Journal of the Royal Statistical Society, Series B, 74, 163–182.
Chung, D., Kuan, P., Li, B., Sanalkumar, R., Liang, K., Bresnick, E., Dewey, C., and Keleş, S. (2011), “Discovering transcription factor binding sites in highly repetitive regions of genomes with multi-read analysis of ChIP-seq data,” PLoS Computational Biology, 7, e1002111.
Liang, K., and Nettleton, D. (2010), “A hidden Markov model approach to testing multiple hypotheses on a tree-transformed Gene Ontology graph,” Journal of the American Statistical Association, 105, 1444–1454.
kun.liang AT uwaterloo.ca