Luo Xiao

Zou, H., Zeng, D., Xiao, L., & Luo, S. (2023). BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA. ANNALS OF APPLIED STATISTICS, 17(3), 2574–2595. https://doi.org/10.1214/23-AOAS1733

Li, R., & Xiao, L. (2023, September 4). Latent factor model for multivariate functional data. BIOMETRICS, Vol. 9. https://doi.org/10.1111/biom.13924

Zou, H., Xiao, L., Zeng, D., & Luo, S. (2023, February 20). Multivariate functional mixed model with MRI data: An application to Alzheimer's disease. STATISTICS IN MEDICINE, Vol. 2. https://doi.org/10.1002/sim.9683

Cui, E., Li, R., Crainiceanu, C. M., & Xiao, L. (2022, October 7). Fast Multilevel Functional Principal Component Analysis. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 10. https://doi.org/10.1080/10618600.2022.2115500

Li, R., Xiao, L., Smirnova, E., Cui, E., Leroux, A., & Crainiceanu, C. M. (2022, May 1). Fixed-effects inference and tests of correlation for longitudinal functional data. STATISTICS IN MEDICINE, Vol. 5. https://doi.org/10.1002/sim.9421

Weaver, C., Xiao, L., & Lu, W. (2022, March 28). Functional data analysis for longitudinal data with informative observation times. BIOMETRICS, Vol. 3. https://doi.org/10.1111/biom.13646

Li, C., Xiao, L., & Luo, S. (2022). Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease. BIOMETRICS, 78(2), 435–447. https://doi.org/10.1111/biom.13427

Chen, H., Caffo, B., Stein-O'Brien, G., Liu, J., Langmead, B., Colantuoni, C., & Xiao, L. (2022, March 31). Two-stage linked component analysis for joint decomposition of multiple biologically related data sets. BIOSTATISTICS, Vol. 3. https://doi.org/10.1093/biostatistics/kxac005

Ma, W., Xiao, L., Liu, B., & Lindquist, M. A. (2021). A functional mixed model for scalar on function regression with application to a functional MRI study. BIOSTATISTICS, 22(3), 439–454. https://doi.org/10.1093/biostatistics/kxz046

Ohuma, E. O., Villar, J., Feng, Y., Xiao, L., Salomon, L., Barros, F. C., … Papageorghiou, A. T. (2021). Fetal growth velocity standards from the Fetal Growth Longitudinal Study of the INTERGROWTH-21 st Project. AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY, 224(2). https://doi.org/10.1016/j.ajog.2020.07.054

Weaver, C., Xiao, L., & Lindquist, M. A. (2021, May 5). Single-index models with functional connectivity network predictors. BIOSTATISTICS, Vol. 5. https://doi.org/10.1093/biostatistics/kxab015

Xiao, L. (2020). Asymptotic properties of penalized splines for functional data. BERNOULLI, 26(4), 2847–2875. https://doi.org/10.3150/20-BEJ1209

Feng, Y., Xiao, L., Li, C., Chen, S. T., & Ohuma, E. O. (2020). Correlation models for monitoring fetal growth. STATISTICAL METHODS IN MEDICAL RESEARCH, 29(10), 2795–2813. https://doi.org/10.1177/0962280220905623

Li, C., Xiao, L., & Luo, S. (2020). Fast covariance estimation for multivariate sparse functional data. STAT, 9(1). https://doi.org/10.1002/sta4.245

Xiao, L., & Nan, Z. (2020). Uniform convergence of penalized splines. STAT, 9(1). https://doi.org/10.1002/sta4.297

Chen, S. T., Xiao, L., & Staicu, A.-M. (2019). A smoothing-based goodness-of-fit test of covariance for functional data. BIOMETRICS, 75(2), 562–571. https://doi.org/10.1111/biom.13005

Xiao, L. (2019). Asymptotic theory of penalized splines. ELECTRONIC JOURNAL OF STATISTICS, 13(1), 747–794. https://doi.org/10.1214/19-EJS1541

Li, C., & Xiao, L. (2019). Optimal design for classification of functional data. Canadian Journal of Statistics, 12. https://doi.org/10.1002/cjs.11531

Anderson, C., Xiao, L., & Checkley, W. (2019). Using data from multiple studies to develop a child growth correlation matrix. STATISTICS IN MEDICINE, 38(19), 3540–3554. https://doi.org/10.1002/sim.7696

Park, S. Y., Xiao, L., Willbur, J. D., Staicu, A.-M., & Jumbe, N. L. (2018). A joint design for functional data with application to scheduling ultrasound scans. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 122, 101–114. https://doi.org/10.1016/j.csda.2018.01.009

Xiao, L. (2018). Asymptotics of bivariate penalised splines. Journal of Nonparametric Statistics, 31(2), 289–314. https://doi.org/10.1080/10485252.2018.1563295

Leroux, A., Xiao, L., Crainiceanu, C., & Checkley, W. (2018). Dynamic prediction in functional concurrent regression with an application to child growth. STATISTICS IN MEDICINE, 37(8), 1376–1388. https://doi.org/10.1002/sim.7582

Xiao, L., Li, C., Checkley, W., & Crainiceanu, C. (2018). Fast covariance estimation for sparse functional data. STATISTICS AND COMPUTING, 28(3), 511–522. https://doi.org/10.1007/s11222-017-9744-8

Xiao, L., Li, C., Checkley, W., & Crainiceanu, C. (2018, May). Fast covariance estimation for sparse functional data (vol 28, pg 511, 2017). STATISTICS AND COMPUTING, Vol. 28, pp. 523–523. https://doi.org/10.1007/s11222-017-9768-0

Grigsby, M. R., Di, J. R., Leroux, A., Zipunnikov, V., Xiao, L., Crainiceanu, C., & Checkley, W. (2018). Novel metrics for growth model selection. Emerging Themes in Epidemiology, 15.

Varma, V. R., Dey, D., Leroux, A., Di, J., Urbanek, J., Xiao, L., & Zipunnikov, V. (2018, January). Total volume of physical activity: TAC, TLAC or TAC(lambda). https://doi.org/10.1016/j.ypmed.2017.10.028

Bai, J., Di, C., Xiao, L., Evenson, K. R., LaCroix, A., Crainiceanu, C., & Buchner, D. M. (2017). AN ACTIVITY INDEX FOR RAW ACCELEROMETRY DATA AND ITS APPLICATION IN OLDER ADULTS. Innovation in Aging, 1(suppl_1), 1239–1239. https://doi.org/10.1093/geroni/igx004.4497

Varma, V. R., Dey, D., Leroux, A., Di, J., Urbanek, J., Xiao, L., & Zipunnikov, V. (2017). Re-evaluating the effect of age on physical activity over the lifespan. PREVENTIVE MEDICINE, 101, 102–108. https://doi.org/10.1016/j.ypmed.2017.05.030

Park, S. Y., Staicu, A.-M., Xiao, L., & Crainiceanu, C. M. (2017). Simple fixed-effects inference for complex functional models. Biostatistics, 19(2), 137–152. https://doi.org/10.1093/biostatistics/kxx026

Huang, L., Reiss, P. T., Xiao, L., Zipunnikov, V., Lindquist, M. A., & Crainiceanu, C. M. (2017). Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data. Biostatistics (Oxford, England), 18(2), 214–229.

Bai, J., Di, C., Xiao, L., Evenson, K. R., LaCroix, A. Z., Crainiceanu, C. M., & Buchner, D. M. (2016). An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics. PLOS ONE, 11(8), 1–14. https://doi.org/10.1371/journal.pone.0160644

Hooker, G., Ramsay, J. O., & Xiao, L. (2016). CollocInfer: Collocation Inference in Differential Equation Models. Journal of Statistical Software, 75(2). https://doi.org/10.18637/jss.v075.i02

Bien, J., Bunea, F., & Xiao, L. (2016). Convex Banding of the Covariance Matrix. Journal of the American Statistical Association, 111(514), 834–845. https://doi.org/10.1080/01621459.2015.1058265

Xiao, L., Zipunnikov, V., Ruppert, D., & Crainiceanu, C. (2016). Fast covariance estimation for high-dimensional functional data. Statistics and Computing, 26(1-2), 409–421. https://doi.org/10.1007/S11222-014-9485-X

Xiao, L., He, B., Koster, A., Caserotti, P., Lange-Maia, B., Glynn, N. W., … Crainiceanu, C. M. (2016). Movement prediction using accelerometers in a human population. BIOMETRICS, 72(2), 513–524. https://doi.org/10.1111/biom.12382

Huang, L., Reiss, P. T., Xiao, L., Zipunnikov, V., Lindquist, M. A., & Crainiceanu, C. M. (2016). Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data. Biostatistics, 8, kxw040. https://doi.org/10.1093/biostatistics/kxw040

Yang, J., Shmuelof, L., Xiao, L., Krakauer, J. W., & Caffo, B. (2015). On tests of activation map dimensionality for fMRI-based studies of learning. Frontiers in Neuroscience, 9. https://doi.org/10.3389/fnins.2015.00085

Bunea, F., & Xiao, L. (2015). On the sample covariance matrix estimator of reduced effective rank population matrices, with applications to fPCA. Bernoulli, 21(2), 1200–1230. https://doi.org/10.3150/14-bej602

Xiao, L., Thurston, S. W., Ruppert, D., Love, T. M. T., & Davidson, P. W. (2014). Bayesian Models for Multiple Outcomes in Domains With Application to the Seychelles Child Development Study. Journal of the American Statistical Association, 109(505), 1–10. https://doi.org/10.1080/01621459.2013.830070

Ma, X., Xiao, L., & Wong, W. H. (2014). Learning regulatory programs by threshold SVD regression. Proceedings of the National Academy of Sciences, 111(44), 15675–15680. https://doi.org/10.1073/pnas.1417808111

Xiao, L., Huang, L., Schrack, J. A., Ferrucci, L., Zipunnikov, V., & Crainiceanu, C. M. (2014). Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach. Biostatistics, 16(2), 352–367. https://doi.org/10.1093/biostatistics/kxu045

Xiao, L., Li, Y., & Ruppert, D. (2013). Fast bivariateP-splines: the sandwich smoother. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(3), 577–599. https://doi.org/10.1111/rssb.12007