Area of research
Statistics and Probability · Information Systems
Research interest
Research interests include Statistical Methods and Inference, Advanced Statistical Methods and Models, Data Mining Algorithms and Applications, and Statistical Methods and Bayesian Inference.
Logistic Regression Tree Analysis
Variable Importance Scores
A Machine-Learning Classification Tree Model of Perceived Organizational Performance in U.S. Federal Government Health Agencies
Machine learning models of tobacco susceptibility and current use among adolescents from 97 countries in the Global Youth Tobacco Survey, 2013-2017.
Subgroup identification for precision medicine: A comparative review of 13 methods
Subgroups from regression trees with adjustment for prognostic effects and postselection inference.
Implementing Clinical Research Using Factorial Designs: A Primer
Toward precision smoking cessation treatment I: Moderator results from a factorial experiment
Identifying effective intervention components for smoking cessation: a factorial screening experiment
Comparative effectiveness of intervention components for producing long‐term abstinence from smoking: a factorial screening experiment
Comparative effectiveness of motivation phase intervention components for use with smokers unwilling to quit: a factorial screening experiment
Enhancing the effectiveness of smoking treatment research: conceptual bases and progress
Fifty Years of Classification and Regression Trees