This article discusses the finite distance properties of three likelihood-based estimation strategies for GARCH processes with non-Gaussian conditional distributions: (1) the maximum likelihood approach; (2) the Quasi Maximum Likelihood approach; (3) a multi-steps recursive estimation approach (REC). We first run a Monte Carlo test which shows that the recursive method may be the most relevant ...

We present an algorithm for recursive estimation of parameters in a mildly nonlinear model involving incomplete data. In particular, we focus on the time-varying deterministic parameters of additive noise in the nonlinear model. For the nonstationary noise that we encounter in robust speech recognition, different observation data segments correspond to different noise parameter values. Hence, r...

Reliable ambiguity resolution in difficult environments such as during setting/rising events of satellites or during limited satellite visibility is a significant challenge for GPS single frequency kinematic relative positioning. Here, a recursive estimation method combining both code and carrier phase measurements was developed that can tolerate recurrent satellite setting/rising and accelerat...

In this paper, we developed the parametric estimation and the self-tuning control problem of the nonlinear systems which are described by discrete-time nonlinear mathematical models, with unknown, time-varying parameters, and operative in a stochastic environment. The parametric estimation is realized by using the prediction error method and the recursive least squares techniques. The self-tuni...

The paper considers a set membership joint estimation of variables and parameters in complex dynamic networks based on parametric uncertain models and limited hard measurements. A recursive estimation algorithm with a moving measurement window is derived that is suitable for on-line network monitoring. The window allows stabilising the classic recursive estimation algorithm and significantly im...

The paper considers a set-membership joint estimation of variables and parameters in complex dynamic networks based on parametric uncertain models and limited hard measurements. The recursive estimation algorithm with moving measurement window is derived that is suitable for on line network monitoring. The window allows stabilising the classic recursive estimation algorithm and significantly im...

in this article, the discrete time state space model with first-order autoregressive dependent process noise is considered and the recursive method for filtering, prediction and smoothing of the hidden state from the noisy observation is designed. the explicit solution is obtained for the hidden state estimation problem. finally, in a simulation study, the performance of the designed method ...

A feedforward control algorithm for active noise control based on the recursive estimation of a generalized finite impulse response (FIR) filter is presented in this paper. Recursive least square estimation (RLSE) with variable forgetting factors is applied to a commercial air ventilation silencer to provide the online estimation of the generalized finite impulse response (FIR) filter to obtain...

This paper presents two stochastic filters considering autoregressive models of first and second order for parameter estimation and system identification. Each model is applied to a reference of the corresponding order and their recursive and non-recursive estimation results are compared; obtaining their error functional values to determine their performance. Due to the recursive methods give b...

This paper is concerned with an observation driven model for time series of counts whose conditional distribution given past observations follows a Poisson distribution. This class of models, called GLARMA, is capable of modeling a wide range of dependence structures and is readily estimated using conditional maximum likelihood. Recursive formulae for carrying out maximum likelihood estimation ...