With the advance of modern technology, more and more data are being recorded
continuously during a time interval or intermittently at several discrete time points. These are …

Computer simulations based on mathematical models have become ubiquitous across the
engineering disciplines and throughout the physical sciences. Successful use of a …

We consider quantile autoregression (QAR) models in which the autoregressive coefficients
can be expressed as monotone functions of a single, scalar random variable. The models …

The varying coefficient models are very important tool to explore the dynamic pattern in
many scientific areas, such as economics, finance, politics, epidemiology, medical science …

Drawing from the authors' own work and from the most recent developments in the field,
Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity …

A global smoothing procedure is developed using basis function approximations for
estimating the parameters of a varying‐coefficient model with repeated measurements …

We consider nonparametric estimation of coefficient functions in a varying coefficient model
of the form Y_ij=X^T_i(t_ij)β(t_ij)+i(t_ij) based on longitudinal observations (Yij, Xi (tij), tij), i …

Semiparametric regression models are very useful for longitudinal data analysis. The
complexity of semiparametric models and the structure of longitudinal data pose new …

Semiparametric models are often considered for analyzing longitudinal data for a good
balance between flexibility and parsimony. In this paper, we study a class of marginal …

Nonparametric varying-coefficient models are commonly used for analyzing data measured
repeatedly over time, including longitudinal and functional response data. Although many …