@article{li_xiao_2023, title={Latent factor model for multivariate functional data}, volume={9}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13924}, abstractNote={Abstract}, journal={BIOMETRICS}, author={Li, Ruonan and Xiao, Luo}, year={2023}, month={Sep} }
 @article{cui_li_crainiceanu_xiao_2022, title={Fast Multilevel Functional Principal Component Analysis}, volume={10}, ISSN={["1537-2715"]}, DOI={10.1080/10618600.2022.2115500}, abstractNote={Abstract We introduce fast multilevel functional principal component analysis (fast MFPCA), which scales up to high dimensional functional data measured at multiple visits. The new approach is orders of magnitude faster than and achieves comparable estimation accuracy with the original MFPCA. Methods are motivated by the National Health and Nutritional Examination Survey (NHANES), which contains minute-level physical activity information of more than 10, 000 participants over multiple days and 1440 observations per day. While MFPCA takes more than five days to analyze these data, fast MFPCA takes less than five minutes. A theoretical study of the proposed method is also provided. The associated function mfpca.face() is available in the R package refund. Supplementary materials for this article are available online.}, journal={JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS}, author={Cui, Erjia and Li, Ruonan and Crainiceanu, Ciprian M. and Xiao, Luo}, year={2022}, month={Oct} }
 @article{li_xiao_smirnova_cui_leroux_crainiceanu_2022, title={Fixed-effects inference and tests of correlation for longitudinal functional data}, volume={5}, ISSN={["1097-0258"]}, DOI={10.1002/sim.9421}, abstractNote={Abstract}, journal={STATISTICS IN MEDICINE}, author={Li, Ruonan and Xiao, Luo and Smirnova, Ekaterina and Cui, Erjia and Leroux, Andrew and Crainiceanu, Ciprian M.}, year={2022}, month={May} }