Data is not just a science, but a practice, a business, and a philosophy.
ABOUT ME
I am currently Associate Professor of Statistics and Business Analytics, Department of Operatioins, Business Analytics and Information Systems, Lindner College of Business, University of Cincinnati, USA. I also serve as Academic Director for the Center for Business Analytics.
My research interests include data fusion and learning, discrete data inference and learning, and their applications in healthcare, medicine, psychology, sociology, information systems, insurance and finance. My research outcomes have been published in top statistics and data science journals including the Journal of American Statistical Association, Annals of Applied Statistics, and Biometrics.
As an award-winning educator in business analytics, my case-study-based classes are well received by students and professionals. Driven by the strong interest in business applications, I have also been leading and directing consulting projects for Fortune 500 companies spanning from healthcare and manufacturing to aviation and insurance companies. In recoganition of my research, teaching, and service, I have received college and university awards including Lindner Research Excellence Emerging Scholar Award (2017), Award for Faculty Excellence (2020), and Daniel E. Westerbeck Junior Faculty Graduate Teaching Award (2021).
STATISTICS AND DATA ANALYTICS RESEARCH
Data fusion and learning
Key words: data integration, evidence synthesis, meta-analysis.
Discrete data inference and learning
Key words: binary data, ordinal data, count data
Association studies (partial and conditional)
Visualization (Partial regressionl plot and QQ plot)
R packages: sure, PAsso, SurrogateRsq
Invited reviews
SELECTED PUBLICATIONS
Liu, D., Zhu, X., Greenwell, B., and Lin, Z. (2023), "A new goodness-of-fit measure for probit models: surrogate R^2", British Journal of Mathematical and Statistical Psychology, 76 (1), 192-210. ->>> read
Liu, D., Liu, R. and Xie, M. (2022), "Nonparametric fusion learning for multi-parameters: synthesize inferences from diverse sources using data depth and confidence distribution", Journal of the American Statistical Association, 117 (540), 2086-2104. ->>> read
Liu, D., Li, S., Yu, Y. and Moustaki, I. (2021), "Assessing partial association between ordinal variables: quantification, visualization and hypothesis testing", Journal of the American Statistical Association, 116 (534), 955-968. ->>> read
Chen, D., Liu, D., Min, X. and Zhang, H. (2020), "Relative efficiency of using summary and individual data in random-effects meta-analysis", Biometrics, 76 (4), 1319-1329. ->>> read
Liu, D. (2019), "Meta-analysis of rare events", Wiley StatsRef: Statistics Reference Online (invited review article). ->>> read
Liu, D. and Zhang, H. (2018), "Residuals and diagnostics for ordinal regression models: a surrogate approach", Journal of the American Statistical Association, 113 (522), 845-854. ->>> read
Zhang, H., Liu, D., Zhao, J. and Bi, X. (2018), "Modeling multivariate traits of comorbidity and genetic studies of alcoholism and nicotine dependence", Annals of Applied Statistics, 12 (4), 2359-2378.
Yang, G., Liu, D., Wang, J. and Xie, M. (2016), "Meta-analysis framework for exact inferences with application to the analysis of rare events'', Biometrics, 72, 1378--1386. ->>> read
Liu, D., Liu, R. and Xie, M. (2015), "Multivariate meta-analysis of heterogeneous studies using only summary statistics: efficiency and robustness'', Journal of the American Statistical Association, 110 (509), 326--340. ->>> read
Liu, D., Liu, R. and Xie, M. (2014), "Exact meta-analysis approach for discrete data and its application to 2 X 2 tables with rare events'', Journal of the American Statistical Association, 109 (508), 1450--1465. ->>> read