This paper considers nonparametric estimation of panel data models using local linear least squares when fixed effects present. The local marginal effect is of the main interest. A withingroup type nonparametric estimator is developed, where the fixed effects are eliminated by subtracting individual specific locally weighted time average (i.e., using the local within transformation). It is show...

In this paper, a nonlinear and/or non-Gaussian smoother utilizing Markov chain Monte Carlo Methods is proposed, where the measurement and transition equations are specified in any general formulation and the error terms in the state-space model are not necessarily normal. The random draws are directly generated from the smoothing densities. For random number generation, the Metropolis-Hastings ...

In this paper, an attempt is made to show a general solution to nonlinear and/or non-Gaussian state space modeling in a Bayesian framework, which corresponds to an extension of Carlin, Polson and Stoffer (1992) and Carter and Kohn (1994, 1996). Using the Gibbs sampler and the Metropolis-Hastings algorithm, an asymptotically exact estimate of the smoothing mean is obtained from any nonlinear and...

Based on a new a posteriori error estimator, an adaptive finite element method is proposed for recovering the Robin coefficient involved in a diffusion system from some boundary measurement. The a posteriori error estimator can not be derived for this ill-posed nonlinear inverse problem as it was done for the existing a posteriori error estimators for direct problems. Instead, we shall derive t...

Efforts in this paper seek to combine graph theory with adaptive dynamic programming (ADP) as a reinforcement learning (RL) framework to determine forward-in-time, realtime, approximate optimal controllers for distributed multi-agent systems with uncertain nonlinear dynamics. A decentralized continuous time-varying control strategy is proposed, using only local communication feedback from two-h...

A Current Output Observer is presented and its estimation error performance is compared to that of the Extended Kalman Filter. It is shown that performance improvement can be obtained by this new scheme with minor increase in computational load. In order to obtain stronger results, scalar nonlinear stochastic systems are focused on. These systems are categorized based on the derivatives of thei...

In this paper a novel hybrid algorithm for harmonics estimation in power systems is proposed. The estimation of the harmonic components is a nonlinear problem due to the nonlinearity of phase of sinusoids in distorted waveforms. Most researchers implemented nonlinear methods to extract the harmonic parameters. However, nonlinear methods for amplitude estimation increase time of convergence. Hen...

Misspecifications (i.e. errors on the parameters) of state space models lead to incorrect inference of the hidden states. This paper studies weakly nonlinear state space models with additive Gaussian noises and proposes a method for detecting and correcting misspecifications. The latter induce a biased estimator of the hidden state but also happen to induce correlation on innovations and other ...

In this paper, a novel stator current based Model Reference Adaptive System (MRAS) estimator for speed estimation in the speed-sensorless vector controlled induction motor drives is presented. In the proposed MRAS estimator, measured stator current of the induction motor is considered as a reference model. The estimated stator current is produced in an adjustable model to compare with the measu...