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My research interest is concerned with the development of statistical methods in the behavioral, biological, and neural sciences. In particular, I am interested in the theory and applications of discrete choice models, meta-analysis, multilevel modeling, resampling methods, state-space modeling, structural equation modeling, and time-series analysis. In collaboration with researchers from different disciplines, I have developed and applied statistical methods to a wide range of empirical problems. One of my current research focuses on developing statistical methods for analysis of massive dataset such as EEG and fMRI data for understanding functional connectivity in the brain.
Education
2003 - Ph.D. in Quantitative Psychology, University of Illinois, Champaign
2000 - M.S. in Statistics, University of Illinois, Champaign
1997 - M.Phil. in Psychology, The Chinese University of Hong Kong
Research Interests
- Statistical Analysis of Brain Imaging Data
- Longitudinal Data Analytic Methods
- Multilevel Analysis
- Mathematical Modeling of Human Decision Making
Selected Publications
Gao, B., Ombao, H., & Ho, R. M. (2009). Cluster analysis for non-stationary time series. In S.Y.Chow, E. Ferrer, & F. Hsieh (Eds.). Statistical methods for modeling human dynamics: An interdisciplinary dialogue. Routldege.
Ho, R. M., Ombao, H., Edgar, C., Canive, J. M., & Miller, G. (2008). Time-frequency discriminant analysis of MEG signals. Neuroimage, 40, 174-186.
Moskowitz, D. S., Ho, R. M., Turocotte-Tremblay, A.-M. (2007). Contextual influences on interpersonal complementarity. Personality and Social Psychology Bulletin, 33, 1051-1063.
Regenwetter, M., Ho, R. M., & Tsetlin, I. (2007). Behavioral social choice analyses of approval voting data in a Thurstonian framework. Psychological Review, 114, 994-1014.
Hawley, L., Ho, R. M., & Zuroff, D., &Blatt, S. (2007). Stress Reactivity Following Brief Treatment for Depression: Differential Effects of Psychotherapy and Medication. Journal of Consulting & Clinical Psychology, 75, 244-256.
Ho, R. M., Shumway, R., & Ombao, H. (2006). State-space approach to modeling dynamical processes: Applications in Biological and Social Sciences. In Walls, T.,& Schafer, J. L. (eds.), Models for intensive longitudinal data (pp.148-175). New York, NY: Oxford University Press.
Ombao, H., & Ho, R. M. (2006). Time-dependent frequency domain principal components analysis of multichannel non-stationary signals. Computational Statistics and Data Analysis, 50, 2339-2360.
Bhattacharya, S., Ho, R.M. & Purkayastha, S. (2006). Bayesian approach to temporal modeling of brain dynamics with fMRI data. Neuroimage, 30, 794-812.
Ho, R. M., Ombao, H., & Shumway, R. (2005). A state-space approach to modeling brain dynamics. Statistica Sinica, 15, 407-425.
Dunteman, G. H. & Ho, R. M. (2005) Generalized linear models: an introduction. Sage University Paper series on Quantitative Applications in the Social Sciences Series. Newsbury Park, CA: Sage.
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