@article{li_xiao_luo_2022, title={Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease}, volume={78}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13427}, abstractNote={Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes, which are correlated and predictive of AD progression. It is of great scientific interest to investigate the association between the outcomes and time to AD onset. We model the multiple longitudinal outcomes as multivariate sparse functional data and propose a functional joint model linking multivariate functional data to event time data. In particular, we propose a multivariate functional mixed model to identify the shared progression pattern and outcome‐specific progression patterns of the outcomes, which enables more interpretable modeling of associations between outcomes and AD onset. The proposed method is applied to the Alzheimer's Disease Neuroimaging Initiative study (ADNI) and the functional joint model sheds new light on inference of five longitudinal outcomes and their associations with AD onset. Simulation studies also confirm the validity of the proposed model. Data used in preparation of this article were obtained from the ADNI database.}, number={2}, journal={BIOMETRICS}, author={Li, Cai and Xiao, Luo and Luo, Sheng}, year={2022}, month={Jun}, pages={435–447} }
 @article{feng_xiao_li_chen_ohuma_2020, title={Correlation models for monitoring fetal growth}, volume={29}, ISSN={["1477-0334"]}, DOI={10.1177/0962280220905623}, abstractNote={Ultrasound growth measurements are monitored to evaluate if a fetus is growing normally compared with a defined standard chart at a specified gestational age. Using data from the Fetal Growth Longitudinal Study of the INTERGROWTH-21st project, we have modelled the longitudinal dependence of fetal head circumference, biparietal diameter, occipito-frontal diameter, abdominal circumference, and femur length using a two-stage approach. The first stage involved finding a suitable transformation of the raw fetal measurements (as the marginal distributions of ultrasound measurements were non-normal) to standardized deviations (Z-scores). In the second stage, a correlation model for a Gaussian process is fitted, yielding a correlation for any pair of observations made between 14 and 40 weeks. The correlation structure of the fetal Z-score can be used to assess whether the growth, for example, between successive measurements is satisfactory. The paper is accompanied by a Shiny application, see https://lxiao5.shinyapps.io/shinycalculator/.}, number={10}, journal={STATISTICAL METHODS IN MEDICAL RESEARCH}, author={Feng, Yuan and Xiao, Luo and Li, Cai and Chen, Stephanie T. and Ohuma, Eric O.}, year={2020}, month={Oct}, pages={2795–2813} }
 @article{li_xiao_luo_2020, title={Fast covariance estimation for multivariate sparse functional data}, volume={9}, ISSN={["2049-1573"]}, DOI={10.1002/sta4.245}, abstractNote={Covariance estimation is essential yet underdeveloped for analysing multivariate functional data. We propose a fast covariance estimation method for multivariate sparse functional data using bivariate penalized splines. The tensor‐product B‐spline formulation of the proposed method enables a simple spectral decomposition of the associated covariance operator and explicit expressions of the resulting eigenfunctions as linear combinations of B‐spline bases, thereby dramatically facilitating subsequent principal component analysis. We derive a fast algorithm for selecting the smoothing parameters in covariance smoothing using leave‐one‐subject‐out cross‐validation. The method is evaluated with extensive numerical studies and applied to an Alzheimer's disease study with multiple longitudinal outcomes.}, number={1}, journal={STAT}, author={Li, Cai and Xiao, Luo and Luo, Sheng}, year={2020} }
 @article{park_li_benavides_heugten_staicu_2019, title={Conditional Analysis for Mixed Covariates, with Application to Feed Intake of Lactating Sows}, volume={2019}, ISSN={["1687-9538"]}, DOI={10.1155/2019/3743762}, abstractNote={We propose a novel modeling framework to study the effect of covariates of various types on the conditional distribution of the response. The methodology accommodates flexible model structure, allows for joint estimation of the quantiles at all levels, and provides a computationally efficient estimation algorithm. Extensive numerical investigation confirms good performance of the proposed method. The methodology is motivated by and applied to a lactating sow study, where the primary interest is to understand how the dynamic change of minute-by-minute temperature in the farrowing rooms within a day (functional covariate) is associated with low quantiles of feed intake of lactating sows, while accounting for other sow-specific information (vector covariate).}, journal={JOURNAL OF PROBABILITY AND STATISTICS}, author={Park, S. Y. and Li, C. and Benavides, S. M. Mendoza and Heugten, E. and Staicu, A. M.}, year={2019}, month={Jul} }
 @article{xiao_li_checkley_crainiceanu_2018, title={Fast covariance estimation for sparse functional data}, volume={28}, ISSN={["1573-1375"]}, DOI={10.1007/s11222-017-9744-8}, abstractNote={Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline smoother that is designed for covariance smoothing and can be used for sparse functional or longitudinal data. We propose a fast algorithm for covariance smoothing using leave-one-subject-out cross-validation. Our simulations show that the proposed method compares favorably against several commonly used methods. The method is applied to a study of child growth led by one of coauthors and to a public dataset of longitudinal CD4 counts.}, number={3}, journal={STATISTICS AND COMPUTING}, publisher={Springer Nature}, author={Xiao, Luo and Li, Cai and Checkley, William and Crainiceanu, Ciprian}, year={2018}, month={May}, pages={511–522} }
 @article{xiao_li_checkley_crainiceanu_2018, title={Fast covariance estimation for sparse functional data (vol 28, pg 511, 2017)}, volume={28}, ISSN={["1573-1375"]}, DOI={10.1007/s11222-017-9768-0}, number={3}, journal={STATISTICS AND COMPUTING}, author={Xiao, Luo and Li, Cai and Checkley, William and Crainiceanu, Ciprian}, year={2018}, month={May}, pages={523–523} }
 @article{li_zhou_2017, title={svt: Singular value thresholding in MATLAB}, volume={81}, number={CN2}, journal={Journal of Statistical Software}, author={Li, C. and Zhou, H.}, year={2017} }