| Twitter data for predicting election results: insights from emotion classification SM Srinivasan, RS Sangwan, CJ Neill, T Zu IEEE Technology and Society Magazine 38 (1), 58-63, 2019 | 14 | 2019 |
| Ultra-high dimensional quantile regression for longitudinal data: an application to blood pressure analysis T Zu, H Lian, B Green, Y Yu Journal of the American Statistical Association 118 (541), 97-108, 2023 | 6 | 2023 |
| Ultra high-dimensional semiparametric longitudinal data analysis B Green, H Lian, Y Yu, T Zu Biometrics 77 (3), 903-913, 2021 | 6 | 2021 |
| Power of predictive analytics: using emotion classification of twitter data for predicting 2016 US presidential elections SM Srinivasan, R Sangwan, C Neill, T Zu The Journal of Social Media in Society 8 (1), 211-230, 2019 | 5 | 2019 |
| Learning to see the hook: Comparing phishing training approaches A Harrison, B Samuel, Z Shan, M Cook, T Zu, D Dawani | 5 | 2019 |
| Leveraging big data platform technologies and analytics to enhance smart city mobility services RG Qiu, T Zu, Y Qian, L Qiu, Y Badr Handbook of Service Science, Volume II, 567-587, 2019 | 5 | 2019 |
| SIQR: An R Package for Single-index Quantile Regression. T Zu, Y Yu R J. 13 (2), 398, 2021 | 1 | 2021 |
| Semiparametric penalized quadratic inference functions for longitudinal data in ultra-high dimensions B Green, H Lian, Y Yu, T Zu Journal of Multivariate Analysis 196, 105175, 2023 | | 2023 |
| gplsim: An R Package for Generalized Partially Linear Single-index Models. T Zu, Y Yu R Journal 15 (1), 2023 | | 2023 |
| Two Essays on Ultra-High-Dimensional Longitudinal Data Analysis and Network Bootstrapping Method T Zu University of Cincinnati, 2023 | | 2023 |
| AIS Electronic Library (AISeL) A Harrison, B Samuel, Z Shan, M Cook, T Zu | | |
