个人信息：

刘耀午，西南财经大学“光华英才”计划特聘教授

西南财经大学统计与数据科学学院，数据科学系

电子邮箱：liuyw AT swufe.edu.cn / yaowuliu615 AT gmail.com

简历：

2017年8月毕业于普渡大学统计学专业，获得统计学博士学位，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与数据科学学院从事教学科研工作，2022年入选国家海外高层次人才引进计划。

主持项目：

• 2024.01—2027.12：国家自然科学基金面上项目《大规模检验中的经验贝叶斯方法》

• 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., & Wang, T. (2025+). A powerful transformation of quantitative responses for biobank-scale association studies. Journal of the American Statistical Association, in press.

• Liu, Y. (2025). A simple and powerful method for large-scale composite null hypothesis testing with applications in mediation analysis. Biometrics, 81, ujaf011.

• Liu, Y., Liu, Z., & Lin, X. (2024). Ensemble methods for testing a global null. Journal of the Royal Statistical Society: Series B, 86, 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, 55, 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, 117, 897-908.

• Li, Z., Liu, Y., & 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, 117, 823-834.

• 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, 19, 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, 109, 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., & Sofer, T. (2021). Genome-wide association study of neck circumference identifies sex-specific loci independent of generalized adiposity. International Journal of Obesity, 45, 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, 115, 393-402.

• Li, X., Li, Z., Zhou, H., Gaynor, S. M., Liu, Y., Chen, H., ..., & 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, 52, 969-983.

• Liu, Y., & Xie, J. (2019). Accurate and efficient p-value calculation via Gaussian approximation: a novel Monte-Carlo method. Journal of the American Statistical Association, 114, 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, 104, 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 association regions in whole genome sequencing studies. The American Journal of Human Genetics, 104, 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, 104, 1404-1433.

• Liu, Y., & Xie, J. (2018). Powerful test based on conditional effects for genome-wide screening. The Annals of Applied Statistics, 12, 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, 77, 104-118.