We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the genera...

This paper examines the issue of state contingency in interest rate linkages across the G7 economies by employing Kalman (1960,1963) filter techniques to estimate time varying parameter models of bilateral dependence. Our results show that there is convincing evidence that international linkages in interest rates vary considerably over time. There are periods when international interest rate mo...

In this paper, we deal with the problem of continuous-time time-varying parameter estimation in stochastic systems, under three different kinds of stochastic perturbations: additive and multiplicative white noise, and coloured noise. The proposed algorithm is based on the least squares method with forgetting factor. Some numerical examples illustrate the effectiveness of the proposed algorithm....

A mathematical model presented in this paper describes the dispersion and concentration ofcontaminants (fine and coarse) in porous medium with the effect of chemical reaction. Solutetransport in porous media is discussed by means of advection-dispersion equation. The effectof particle mass parameter and reaction rate parameter on the dispersion coefficient and meanconcentration of a chemical so...

Parametric modeling permits an efficient representation of audio signals and is increasingly utilized for very low bit rate coding applications. Such systems are based on a decomposition of the audio signal into components that are described by appropriate source models and represented by model parameters. Commonly used components types are sinusoidal trajectories, harmonic tones, transients, a...

In this paper, we address the analysis and state-feedback synthesis problems for linear parameter-varying (LPV) systems with parameter-varying time delays. It is assumed that the state-space data and the time delays are dependent on parameters that are measurable in real-time and vary in a compact set with bounded variation rates. We explore the stability and the induced L 2 norm performance of...

Many systems arising in biological applications are subject to periodic forcing. In these systems the forcing parameter is not only time-varying but also known to have a periodic structure. We present an approach to estimate periodic, time-varying parameters using nonlinear Bayesian filtering. Most parameter estimation methodology in the literature is aimed at estimating constant parameters or ...

Purpose – This paper is concerned with an online parameter estimation algorithm for nonlinear uncertain time-varying systems for which no stochastic information is available. Design/methodology/approach – The estimation procedure, called nonlinear learning rate adaptation (NLRA), computes an individual adaptive learning rate for each parameter instead of using a single adaptive learning rate fo...

Parameter estimation of time-varying non-Gaussian autoregressive processes can be a highly nonlinear problem. The problem gets even more difficult if the functional form of the time variation of the process parameters is unknown. In this paper, we address parameter estimation of such processes by particle filtering, where posterior densities are approximated by sets of samples (particles) and p...

in this study, the stability problem of a circular orthotropic cylindrical shell under the effect of anaxial compression varying with a power function of time is considered. at first, the modified donnell typedynamic stability and compatibility equations are obtained using love’s shell theory. applying the galerkinmethod and rayleigh-ritz variational techniques to these equations and taking the...