Space-time varying coefficient models, which are used to identify the effects of covariates that change over time and spatial location, have been widely studied in recent years. One such model, called quantile regression is particularly useful when dealing with outliers or non-standard conditional distributions data. However, functions not easily specified a parametric manner, nonparametric tec...

We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of terminal event that is subject to right censoring. The coefficients capture trajectories covariate effects along both followup time and residual lifetime. proposed extends parametric conditional approach given in recent literature, thus avoids potential misspecification. cons...

This paper is a study of high-throughput filter structures such as block structures and their behavior in finite precision environments. Block structures achieve high throughput rates by using a large number of processors working in parallel. It has been believed that block structures which are relatively robust to round-off noise must also be robust to coefficient quantization errors. However,...

evolving models have found applications in many real world systems. in this paper, potentials of the evolving linear models (elms) in tracking control design for nonlinear variable structure systems are introduced. at first, an elm is introduced as a dynamic single input, single output (siso) linear model whose parameters as well as dynamic orders of input and output signals can change through ...

tion (DMS-08-06199). We are grateful to two referees and an Associate Editor for their constructive comments and careful reading. Summary The proposed functional varying coefficient model provides a versatile and flexible analysis tool for relating longitudinal responses to longitudinal predictors. Specifically , this approach provides a novel representation of varying coefficient functions thr...