We propose marginal integration estimation and testing methods for the coefficients of varying-coefficient multivariate regression models. Asymptotic distribution theory is developed for the estimation method, which enjoys the same rate of convergence as univariate function estimation. For the test statistic, asymptotic normal theory is established. These theoretical results are derived under t...

A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF) hysteresis characteristics and polarization characteristics is used. The effect of the robust EKF gain coefficient on SOC estimation is analy...

We discuss nonparametric estimation of the distribution function G(x) of the autoregressive coefficient from a panel of N random-coefficient AR(1) data, each of length n, by the empirical distribution of lag 1 sample correlations of individual AR(1) processes. Consistency and asymptotic normality of the empirical distribution function and a class of kernel density estimators is established unde...

This dissertation contains two projects that are related to varying coefficient models. The traditional least squares based kernel estimates of the varying coefficient model will lose some efficiency when the error distribution is not normal. In the first project, we propose a novel adaptive estimation method that can adapt to different error distributions and provide an efficient EM algorithm ...

In this paper, we introduce the stickiness coefficient, a summary statistic for time-course and longitudinal data, which is designed to characterize the time dynamics of such data. The stickiness coefficient provides a simple, intuitive and informative measure that captures key information contained in time-course data. Under the assumption that the data are generated by the trajectories of a s...

In this paper, an actuator fault estimation problem is tackled, based on the LPV approach. First, the actuator fault is modeled in the form of a multiplicative fault by using a constant coefficient representing the loss of efficiency of the actuator’s power α. Therefore, the estimation of a time varying faulty actuator can be transformed into the estimation of a constant coefficient α. Then, th...