个人信息：

刘耀午，教授

西南财经大学统计学院，数据科学系

电子邮箱：liuyw@haoyii.com / yaowuliu615@gmail.com

简历：

2017年8月毕业于普渡大学统计学专业，取得统计学博士学位，2017年8月至2019年10月在哈佛大学读博士后，同年10月进入西南财经大学统计学院从事教学科研工作。

主持项目：

• 2021.01-2023.12：国家自然科学基金青年科学基金项目《大规模遗传关联性分析中的假设检验方法》
  

代表性论文：

• Liu, Y., Liu, Z., & Lin, X. (2024) Ensemble methods for testing a global null. Journal of the Royal Statistical Society: Series B (Statistical Methodology). Vol. 86, pp. 461-486.

• Li, X., Quick, C., Zhou, H., Gaynor, S., Liu, Y., Chen, H., …, Li, Z., Lin, X. (2023). Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole-genome sequencing studies. Nature Genetics, Vol. 55, pp. 155-164.

• Liu, Y., Li, Z., & Lin, X. (2022) A Minimax Optimal Ridge-Type Set Test for Global Hypothesis with Applications in Whole Genome Sequencing Association Studies. Journal of the American Statistical Association. Vol. 117, pp. 897-908.
  

• Li, Z., Liu, Y., and Lin, X. (2022) Simultaneous Detection of Signal Regions Using Quadratic Scan Statistics With Applications in Whole Genome Association Studies. Journal of the American Statistical Association. Vol. 117, pp. 823-934.

• Li, Z., Li, X., Zhou, H., Gaynor, S.M., Selvaraj, M., Arapoglou, T., Quick, C., Liu, Y., Chen, H., …, Lin, X. (2022). A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies. Nature Methods, Vol. 19, pp. 1599–1611.

• Li, X., Yung, G., Zhou, H., Sun, R., Li, Z., Liu, Y., Ionita-Laza,I., Lin, X. (2022). A Multi-dimensional Integrative Scoring Framework for Predicting Functional Regions in the Human Genome. The American Journal of Human Genetics. Vol. 109, pp. 446-456.

• Liu, Y., Zhang, X., Lee, J., Smelser, D., Cade, B., Chen, H., Zhou, H., Kirchner, H.L., Lin, X., Mukherjee, S., Hillman, D., Liu, C., Redline, S. and, Sofer, T. (2021). Genome-wide association study of neck circumference identifies sex-specific loci independent of generalized adiposity. International Journal of Obesity. Vol. 45, pp. 1532-1541.

• Liu, Y. & Xie, J. (2020). Cauchy combination test: A powerful test with analytic p-value calculation under arbitrary dependency structures, Journal of the American Statistical Association, Vol. 115, pp. 393-402.

• Li, X., Li, Z., Zhou, H., Gaynor, S.M., Liu, Y., Chen, H.,…, and Lin, X. (2020). Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale, Nature Genetics, Vol. 52, pp. 969-983.

• Liu, Y. & Xie, J. (2019). Accurate and effcient p-value calculation via Gaussian approximation: a novel Monte-Carlo method, Journal of the American Statistical Association, Vol. 114, pp. 384-392.

• Liu, Y., Chen, S., Li, Z., Morrison, A.C., Boerwinkle, E., & Lin, X. (2019). ACAT: a fast and powerful p-value combination method for rare-variant analysis in sequencing studies, The American Journal of Human Genetics, Vol. 104, pp. 410-421.

• Li, Z., Li, X., Liu, Y., Shen, J., Chen, H., Zhou, H., Morrison, A.C., Boerwinkle, E., & Lin, X. (2019). Dynamic scan procedure for detecting rare-variant associa-tion regions in whole genome sequencing studies, The American Journal of Human Genetics, Vol. 104, pp. 802-814.

• Su, R., Zhang, Q, Liu, Y. & Tay, L. (2019). Modeling congruence in organizational research with latent moderated structural equations, Journal of Applied Psychology, Vol. 104, pp. 1404-1433.

• Liu, Y. & Xie, J. (2018). Powerful test based on conditional effects for genome-wide screening, The Annals of Applied Statistics, Vol. 12, pp. 567.

• Cao, M., Tay, L. & Liu, Y. (2017). A Monte Carlo study of an iterative Wald test procedure for DIF analysis, Educational and Psychological Measurement, Vol. 77, pp. 104-118.