Jicai Liu | Data Science | Women Researcher Award Published on 20/09/2025 by Popular Scientist Assoc. Prof. Dr. Jicai Liu | Data Science | Women Researcher Award Associate Professor | Shanghai Lixin University of Accounting and Finance | China Jicai Liu is an Associate Professor of Statistics at the School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, with a research focus on high-dimensional data, survival analysis, dimension reduction, and quantile regression. His academic journey includes advanced training in statistics and extensive teaching and research experience across leading institutions in China and collaborations abroad. He has contributed to the development of novel methodologies in statistical theory and applications, particularly in areas such as high-dimensional regression, nonparametric tests, hazards models, feature screening, clustering algorithms, and dimension reduction techniques. His publications appear in internationally recognized journals including Bernoulli, Science China Mathematics, Journal of Computational and Graphical Statistics, Journal of Multivariate Analysis, Computational Statistics and Data Analysis, and Statistics and Computing, among others. As corresponding author on multiple works, he has advanced methods for analyzing censored outcomes, martingale difference correlation, projection quantile correlation, and sufficient dimension reduction. His contributions also extend to robust estimation, survival models for multivariate failure time data, additive hazards models, and semi-supervised regression. Through his research, he has established a strong reputation in both theoretical developments and practical applications, providing statistical tools that address complex data structures and real-world problems. With 357 citations by 262 documents across 33 publications and an h-index of 10, he has demonstrated significant scholarly impact. In addition to his academic achievements, he has been engaged in collaborative projects with international partners and short-term academic visits, enriching his global perspective and research impact. His work continues to influence the fields of statistics and applied mathematics, contributing innovative approaches to modern statistical challenges and advancing the understanding of high-dimensional and survival data analysis. Profile: Scopus | Orcid Featured Publications Liu, J. (2022). Estimation under single-index hazards models: A new nonparametric extension of ANOVA via projection mean variance measure. Statistica Sinica. Liu, J. (2022). K-CDFs: A nonparametric clustering algorithm via cumulative distribution function. Journal of Computational and Graphical Statistics. Liu, J., Si, Y., Niu, Y., & Zhang, R. (2022). Projection quantile correlation and its use in high-dimensional grouped variable screening. Computational Statistics & Data Analysis, 107369. Niu, Y., Zhang, R., Liu, J., & Li, H. (2020). Group screening for ultra-high-dimensional feature under linear model. Statistical Theory and Related Fields, 4(2), 120–132. Zhang, Y., Liu, J., Wu, Y., & Fang, X. (2019). A martingale-difference-divergence-based estimation of central mean subspace. Statistics and Its Interface, 12(4), 571–584.
Jicai Liu is an Associate Professor of Statistics at the School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, with a research focus on high-dimensional data, survival analysis, dimension reduction, and quantile regression. His academic journey includes advanced training in statistics and extensive teaching and research experience across leading institutions in China and collaborations abroad. He has contributed to the development of novel methodologies in statistical theory and applications, particularly in areas such as high-dimensional regression, nonparametric tests, hazards models, feature screening, clustering algorithms, and dimension reduction techniques. His publications appear in internationally recognized journals including Bernoulli, Science China Mathematics, Journal of Computational and Graphical Statistics, Journal of Multivariate Analysis, Computational Statistics and Data Analysis, and Statistics and Computing, among others. As corresponding author on multiple works, he has advanced methods for analyzing censored outcomes, martingale difference correlation, projection quantile correlation, and sufficient dimension reduction. His contributions also extend to robust estimation, survival models for multivariate failure time data, additive hazards models, and semi-supervised regression. Through his research, he has established a strong reputation in both theoretical developments and practical applications, providing statistical tools that address complex data structures and real-world problems. With 357 citations by 262 documents across 33 publications and an h-index of 10, he has demonstrated significant scholarly impact. In addition to his academic achievements, he has been engaged in collaborative projects with international partners and short-term academic visits, enriching his global perspective and research impact. His work continues to influence the fields of statistics and applied mathematics, contributing innovative approaches to modern statistical challenges and advancing the understanding of high-dimensional and survival data analysis. Profile: Scopus | Orcid Featured Publications Liu, J. (2022). Estimation under single-index hazards models: A new nonparametric extension of ANOVA via projection mean variance measure. Statistica Sinica. Liu, J. (2022). K-CDFs: A nonparametric clustering algorithm via cumulative distribution function. Journal of Computational and Graphical Statistics. Liu, J., Si, Y., Niu, Y., & Zhang, R. (2022). Projection quantile correlation and its use in high-dimensional grouped variable screening. Computational Statistics & Data Analysis, 107369. Niu, Y., Zhang, R., Liu, J., & Li, H. (2020). Group screening for ultra-high-dimensional feature under linear model. Statistical Theory and Related Fields, 4(2), 120–132. Zhang, Y., Liu, J., Wu, Y., & Fang, X. (2019). A martingale-difference-divergence-based estimation of central mean subspace. Statistics and Its Interface, 12(4), 571–584.