Many modern large-scale longitudinal neuroimaging studies, such as the Alzheimer's
Disease Neuroimaging Initiative (ADNI) study, have collected/are collecting asynchronous …

We study the transformed hazards model with intermittently observed time-dependent
covariates for the censored outcome. Existing work assumes the availability of the whole …

Motivated by the analysis of longitudinal neuroimaging studies, we study the longitudinal
functional linear regression model under asynchronous data setting for modeling the …

Forecasting in economic data analysis is dominated by linear prediction methods where the
predicted values are calculated from a fitted linear regression model. With multiple predictor …

We study partially linear models for asynchronous longitudinal data to incorporate nonlinear
time trend effects. Local and global estimating equations are developed for estimating the …

We address estimation of the marginal effect of a time-varying binary treatment on a
continuous longitudinal outcome in the context of observational studies using electronic …

A great deal of literature has been established for regression analysis of longitudinal data
but most of the existing methods assume that covariates can be observed completely or at …

We consider the problem of estimating multiple change points for a functional data process.
There are numerous examples in science and finance in which the process of interest may …

In linear models, omitting a covariate that is orthogonal to covariates in the model does not
result in biased coefficient estimation. This generally does not hold for longitudinal data …

We consider varying-coefficient models for mixed synchronous and asynchronous
longitudinal covariates, where asynchronicity refers to the misalignment of longitudinal …