Luo Xiao

A Functional Joint Model for Survival and Multivariate Sparse Functional Data in Multi‐Cohort Alzheimer's Disease Study

Wang, W., Xiao, L., Li, R., Luo, S., & Initiative, A. D. N. (2026, February 1). Statistics in Medicine.

Dynamic prediction with multivariate longitudinal outcomes and longitudinal magnetic resonance imaging data

Zou, H., Xiao, L., Zeng, D., & Luo, S. (2025, March 1). The Annals of Applied Statistics, Vol. 19, pp. 505–528.

Sparse and integrative principal component analysis for multiview data

Xiao, L., & Xiao, L. (2024, January 1). Electronic Journal of Statistics, Vol. 18, pp. 3774–3824.

Bayesian inference and dynamic prediction for multivariate longitudinal and survival data

Zou, H., Zeng, D., Xiao, L., & Luo, S. (2023, September 1). The Annals of Applied Statistics, Vol. 17, pp. 2574–2595.

Latent Factor Model for Multivariate Functional Data

Li, R., & Xiao, L. (2023, September 4). Biometrics, Vol. 9.

Multivariate functional mixed model with MRI data: An application to Alzheimer's disease

Zou, H., Xiao, L., Zeng, D., Luo, S., & Alzheimer's Disease Neuroimaging Initiative. (2023, February 20). Statistics in Medicine, Vol. 2.

Fast Multilevel Functional Principal Component Analysis

Cui, E., Li, R., Crainiceanu, C. M., & Xiao, L. (2022, August 22). Journal of Computational and Graphical Statistics, Vol. 10.

Fixed‐effects inference and tests of correlation for longitudinal functional data

Li, R., Xiao, L., Smirnova, E., Cui, E., Leroux, A., & Crainiceanu, C. M. (2022, May 1). Statistics in Medicine, Vol. 5.

Functional Data Analysis for Longitudinal Data with Informative Observation Times

Weaver, C., Xiao, L., & Lu, W. (2022, February 21). Biometrics, Vol. 3.

Two-stage linked component analysis for joint decomposition of multiple biologically related data sets

Chen, H., Caffo, B., Genevieve, S.-O. B., Liu, J., Langmead, B., Colantuoni, C., & Xiao, L. (2022, January 31). Biostatistics, Vol. 3.

Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease

Li, C., Xiao, L., & Luo, S. (2021, January 27). Biometrics.

Single-index models with functional connectivity network predictors

Weaver, C., Xiao, L., & Lindquist, M. A. (2021, March 26). Biostatistics, Vol. 5.

Asymptotic properties of penalized splines for functional data

Xiao, L. (2020, August 27). Bernoulli, Vol. 26, pp. 2847–2875.

Correlation models for monitoring fetal growth

Feng, Y., Xiao, L., Li, C., Chen, S. T., & Ohuma, E. O. (2020, March 23). Statistical Methods in Medical Research.

Fast covariance estimation for multivariate sparse functional data

Li, C., Xiao, L., & Luo, S. (2020, January 1). Stat, Vol. 9.

Fetal growth velocity standards from the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project

Ohuma, E. O., Villar, J., Feng, Y., Xiao, L., Salomon, L., Barros, F. C., … Carvalho, M. (2020, August 5). American Journal of Obstetrics and Gynecology, Vol. 224.

Uniform convergence of penalized splines

Xiao, L., & Nan, Z. (2020, January 1). Stat, Vol. 9.

A functional mixed model for scalar on function regression with application to a functional MRI study

Ma, W., Xiao, L., Liu, B., & Lindquist, M. A. (2019, October 4). Biostatistics, Vol. 10.

Asymptotic theory of penalized splines

Xiao, L. (2019, January 1). Electronic Journal of Statistics, Vol. 13, pp. 747–794.

Optimal design for classification of functional data

Li, C., & Xiao, L. (2019, December 19). Canadian Journal of Statistics, Vol. 12.

A Smoothing-Based Goodness-of-Fit Test of Covariance for Functional Data

Chen, S. T., Xiao, L., & Staicu, A.-M. (2018, November 19). Biometrics, Vol. 75, pp. 562–571.

A joint design for functional data with application to scheduling ultrasound scans

Park, S. Y., Xiao, L., Willbur, J. D., Staicu, A.-M., & Jumbe, N. L. (2018, January 31). Computational Statistics & Data Analysis, Vol. 122, pp. 101–114.

Asymptotics of bivariate penalised splines

Journal of Nonparametric Statistics, 31(2), 289–314.

Novel metrics for growth model selection

Emerging Themes in Epidemiology, 15.

Using data from multiple studies to develop a child growth correlation matrix

Anderson, C., Xiao, L., & Checkley, W. (2018, April 26). Statistics in Medicine, Vol. 4, pp. 3540–3554.

AN ACTIVITY INDEX FOR RAW ACCELEROMETRY DATA AND ITS APPLICATION IN OLDER ADULTS

Bai, J., Di, C., Xiao, L., Evenson, K. R., LaCroix, A., Crainiceanu, C., & Buchner, D. M. (2017, June 30). Innovation in Aging, Vol. 1, pp. 1239–1239.

Dynamic prediction in functional concurrent regression with an application to child growth

Leroux, A., Xiao, L., Crainiceanu, C., & Checkley, W. (2017, December 11). Statistics in Medicine, Vol. 37, pp. 1376–1388.

Erratum to: Fast covariance estimation for sparse functional data

Xiao, L., Li, C., Checkley, W., & Crainiceanu, C. (2017, August 23). Statistics and Computing, Vol. 28, pp. 523–523.

Fast covariance estimation for sparse functional data

Xiao, L., Li, C., Checkley, W., & Crainiceanu, C. (2017, April 11). Statistics and Computing, Vol. 4, pp. 511–522.

Re-evaluating the effect of age on physical activity over the lifespan

Varma, V. R., Dey, D., Leroux, A., Di, J., Urbanek, J., Xiao, L., & Zipunnikov, V. (2017, June 2). Preventive Medicine, Vol. 101, pp. 102–108.

Simple fixed-effects inference for complex functional models

Biostatistics, 19(2), 137–152.

Total volume of physical activity: TAC, TLAC or TAC( λ )

Varma, V. R., Dey, D., Leroux, A., Di, J., Urbanek, J., Xiao, L., & Zipunnikov, V. (2017, November 11). Preventive Medicine, Vol. 106, pp. 233–235.

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.

CollocInfer: Collocation Inference in Differential Equation Models

Hooker, G., Ramsay, J. O., & Xiao, L. (2016, January 1). Journal of Statistical Software, Vol. 75.

An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics

Bai, J., Di, C., Xiao, L., Evenson, K. R., LaCroix, A. Z., Crainiceanu, C. M., & Buchner, D. M. (2016, August 11). PLoS ONE, Vol. 11, pp. 1–14.

An activity index for raw accelerometry data and its comparison with other activity metrics

PLoS One, 11(8).

Collocinfer: collocation inference in differential equation models

Journal of Statistical Software, 75(2).

Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data

Huang, L., Reiss, P. T., Xiao, L., Zipunnikov, V., Lindquist, M. A., & Crainiceanu, C. M. (2016, August 29). Biostatistics, Vol. 8, p. kxw040.

Convex Banding of the Covariance Matrix

Journal of the American Statistical Association, 111(514), 834–845.

Movement Prediction Using Accelerometers in a Human Population

Xiao, L., He, B., Koster, A., Caserotti, P., Lange-Maia, B., Glynn, N. W., … Crainiceanu, C. M. (2015, August 19). Biometrics, Vol. 72, pp. 513–524.

On tests of activation map dimensionality for fMRI-based studies of learning

Yang, J., Shmuelof, L., Xiao, L., Krakauer, J. W., & Caffo, B. (2015, April 14). Frontiers in Neuroscience, Vol. 9.

On the sample covariance matrix estimator of reduced effective rank population matrices, with applications to fPCA

Bunea, F., & Xiao, L. (2015, April 21). Bernoulli, Vol. 21, pp. 1200–1230.

Fast covariance estimation for high-dimensional functional data

Statistics and Computing, 26(1-2), 409–421.

Learning regulatory programs by threshold SVD regression

Ma, X., Xiao, L., & Wong, W. H. (2014, October 20). Proceedings of the National Academy of Sciences, Vol. 111, pp. 15675–15680.

Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional approach

Xiao, L., Huang, L., Schrack, J. A., Ferrucci, L., Zipunnikov, V., & Crainiceanu, C. M. (2014, October 30). Biostatistics, Vol. 16, pp. 352–367.

Bayesian Models for Multiple Outcomes in Domains With Application to the Seychelles Child Development Study

Xiao, L., Thurston, S. W., Ruppert, D., Love, T. M. T., & Davidson, P. W. (2013, August 14). Journal of the American Statistical Association, Vol. 109, pp. 1–10.

Fast BivariateP-Splines: The Sandwich Smoother

Xiao, L., Li, Y., & Ruppert, D. (2013, February 7). Journal of the Royal Statistical Society Series B (Statistical Methodology), Vol. 75, pp. 577–599.