07/21/2017 @ 10:30-11:15
Research Talk by Francesca Chiaromonte
Statistics for large, complex data and its role in “Omics” research 
1. Liu, Y., Chiaromonte, F. and Li, B., 2016. Structured Ordinary Least Squares: A Sufficient Dimension Reduction approach for regressions with partitioned predictors and heterogeneous units. Biometrics.
2. Guo, Z., Li, L., Lu, W. and Li, B., 2015. Groupwise dimension reduction via envelope method. Journal of the American Statistical Association, 110(512), pp.1515-1527.
3. Ma, Y. and Zhu, L., 2013. A review on dimension reduction. International Statistical Review, 81(1), pp.134-150.
4. Bertsimas, D., King, A. and Mazumder, R., 2016. Best subset selection via a modern optimization lens. The Annals of Statistics, 44(2), pp.813-852.
5. Campos-Sánchez, R., Cremona, M.A., Pini, A., Chiaromonte, F. and Makova, K.D., 2016. Integration and fixation preferences of human and mouse endogenous retroviruses uncovered with functional data analysis. PLoS Comput Biol, 12(6), p.e1004956.
6. Cremona, M.A., Sangalli, L.M., Vantini, S., Dellino, G.I., Pelicci, P.G., Secchi, P. and Riva, L., 2015. Peak shape clustering reveals biological insights. BMC bioinformatics, 16(1), p.349.
7. Tibshirani, R., 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288.
8. Zou, H. and Hastie, T., 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), pp.301-320.
Francesca Chiaromonte | Statistics for large, complex data and its role in “Omics” research | CGSI 2017