101 citations · 230 across the 12 of their papers we have counts for
9 papers · 1 filter
Multivariate Multinomial Logit Model with ANOVA Decomposition for Correlated Categorical Outcomes
Sohyeon Kim, Luo Xiao, Xinming An +1
In medical research, patients often have multiple interdependent outcomes, such as posttraumatic stress disorder (PTSD), depression, and pain among trauma survivors. Most existing…
Joint Model for Survival and Multivariate Sparse Functional Data with Application to a Study of Alzheimer's Disease
Cai Li, Luo Xiao, Sheng Luo
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…
Model Testing for Generalized Scalar-on-Function Linear Models
Stephanie T. Chen, Luo Xiao, Ana-Maria Staicu
Scalar-on-function linear models are commonly used to regress functional predictors on a scalar response. However, functional models are more difficult to estimate and interpret th…
An Approximate Restricted Likelihood Ratio Test for Variance Components in Generalized Linear Mixed Models
Stephanie T. Chen, Luo Xiao, Ana-Maria Staicu
Generalized linear mixed models (GLMMs) are used to model responses from exponential families with a combination of fixed and random effects. For variance components in GLMMs, we p…
Fast Covariance Estimation for Multivariate Sparse Functional Data
Cai Li, Luo Xiao, Sheng Luo
Covariance estimation is essential yet underdeveloped for analyzing multivariate functional data. We propose a fast covariance estimation method for multivariate sparse functional…
Simple fixed-effects inference for complex functional models
So Young Park, Ana-Maria Staicu, Luo Xiao +1
We propose simple inferential approaches for the fixed effects in complex functional mixed effects models. We estimate the fixed effects under the independence of functional residu…
Fast Covariance Estimation for Sparse Functional Data
Luo Xiao, Cai Li, William Checkley +1
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 associate…
Fast Covariance Estimation for High-dimensional Functional Data
Luo Xiao, David Ruppert, Vadim Zipunnikov +1
For smoothing covariance functions, we propose two fast algorithms that scale linearly with the number of observations per function. Most available methods and software cannot smoo…
Fast Bivariate Penalized Splines: the Sandwich Smoother
Luo Xiao, Yingxing Li, David Ruppert
We propose a fast penalized spline method for bivariate smoothing. Univariate P-spline smoothers (Eilers and Marx, 1996) are applied simultaneously along both coordinates. The new…