Peter Song is a Professor of Biostatistics at the Department of Biostatistics, School of Public Health, University of Michigan. He received his PhD in Statistics from the University of British Columbia in 1996. Prior to the appointment at the University of Michigan, he was a faculty member at the Department of Statistics and Actuarial Science, University of Waterloo (2004-2007) and a faculty member at the Department of Mathematics and Statistics, York University, Toronto (1996-2004). Peter Song's research interests include bioinformatics, longitudinal data analysis, missing data problems in clinical trials, statistical genetics, and time series analysis. He is interested in methodological developments related to modelling, statistical inference and applications in biomedical sciences. In particular, Dr. Song's research projects are strongly motivated from real world data analysis. In 2007 he published a monograph "Correlated Data Analysis: Modeling, Analytics and Applications" by Springer.
- Ph.D., Statistics, University of British Columbia, 1996
- B.S., Statistics, Jilin University, 1985
Research Interests & Projects
My research interests lie in two major fields: In the field of statistical methodology, my interests include composite likelihood method, copula, data integration, generalized linear models, longitudinal data analysis, missing data, statistical computing, spatial/spatio-temporal data analysis, and time series analysis. In the field of empirical study, my interests include asthma, bioinformatics, biomarker, chronic disease, epigenetics, environmental health sciences, nephrology, obesity, and statistical genetics.
- Wang, F., Song, P.X.K. and Wang, L. (2015). Merging multiple longitudinal studies with study-specific missing covariates: A joint estimating function approach. Biometrics, to appear.
- Ma, S. and Song, P. X.K. (2015). Varying index coefficient models. Journal of the American Statistical Association 110,341-356.
- Bai, Y., Kang, J. and Song, P,X.K. (2014). Efficient pairwise composite likelihood estimation for spatial-clustered data. Biometrics 70, 661-670.
- Bai, Y., Song, P.X.-K. and Raghunathan, T.E. (2012). Joint composite estimating functions in spatiotemporal models. Journal of the Royal Statistical Society Series B. 74, 799-824.
- Zhou, Q.M., Song, P.X.-K. and Thompson, M.E. (2012). Information ratio test for model misspecification in quasi-likelihood inference. Journal of the American Statistical Association 107, 205-213.
- Wang, F., Wang, L. and Song, P.X.-K. (2012). Quadratic inference function approach to merging longitudinal studies: Validation test and joint estimation. Biometrika 99, 755-762.
- Zhu, B., Taylor, J.M.G. and Song, P.X.-K. (2011). Semiparametric stochastic modeling of the rate function in longitudinal studies. Journal of the American Statistical Association 106, 1485-1495.
- Gao, X and Song, P.X.-K. (2010). Composite likelihood Bayesian information criteria for model selection in high dimensional data. Journal of the American Statistical Association 105, 1531-1540.
- Zhang, P., Wang, X. and Song, P.X.K. (2006). Clustering categorical data based on distance vectors. Journal of American Statistical Association 101, 355-367.
- Song, P.X.K., Fan, Y. and Kalbfleisch, J.D. (2005). Maximization by parts in likelihood inference (with discussion). Journal of American Statistical Association 100, 1145-1167.