We give a general formulation of a non-Gaussian conditional linear AR(1) model subsuming most of the non-Gaussian AR(1) models that have appeared in the literature. We derive some general results giving properties for the stationary process mean, variance and correlation structure, and conditions for stationarity. These results highlight similarities and differences with the Gaussian AR(1) mode...

As we have remarked, dependence is very common in time series observations. To model this time series dependence, we start with univariate ARMA models. To motivate the model, basically we can track two lines of thinking. First, for a series xt, we can model that the level of its current observations depends on the level of its lagged observations. For example, if we observe a high GDP realizati...

This technical report addresses model-based interpolation of long signal gaps. It demonstrates that employing a modified autoregressive AR model, computed as a weighted sum of line spectral pair (LSP) polynomials, is more efficient computationally than using a conventional AR model, since longer signal gaps can be interpolated at reduced model order. Key-words: acoustic signal processing, audio...

An iterative procedure for computation of stationary density of autoregressive processes is proposed. On an example with exponentially distributed white noise it is demonstrated that the procedure converges geometrically fast. The AR(1) and AR(2) models are analyzed in detail.

AbstractWe present a two-step maximum likelihood (TSML) algorithm for blind identification of single-inputmultiple-output (SIMO) channels modeled as autoregressive (AR) system. The AR-TSML algorithm provides a new and useful alternative to a previously developed TSML algorithm for moving-average (MA) system. The AR-TSML algorithm is shown to be more robust than the MA-TSML algorithm if the chan...

This work addresses the influence of point spectrum on large sample statistics of the autoregressive spectral estimator. In particular, the asymptotic distributions of the AR coefficients, the innovations variance, and the spectral density estimator of a finite order AR(p) model for a mixed spectrum process are presented. Numerical simulations are performed to verify the analytical results.

The statistical properties of the Autoregressive distance between ARIMA processes are investigated. In particular, the asymptotic distribution of the squared AR distance and an approximation which is computationally efficient are derived. Moreover, the problem of time series clustering and classification is discussed and the performance of the AR distance is illustrated by means of some empiric...

In this paper, recent applications of autoregressive (AR) and adaptive autoregressive (TVAR) models to EEG signals for detection of epileptic seizures are addressed. First of all, AR/TVAR models based the complexity measure with the order of AR model and the spectrum estimation with online AR model are introduced and employed to analyze the EEG signals with epileptic seizures. Then, three new a...

A computationally efficient speech enhancement method is proposed. Reduction of computations is achieved due to derived properties of block model of autoregressive (AR) signal. Decreasing of filtering error in comparison with traditional Kalman filter is shown. The problem of estimation of speech AR parameters is also considered. A two-phase computationally efficient estimation procedure, based...

Statistical analysis and performance modeling of compressed video traffic streams are efficient tools to estimate network resources and to predict network behavior under various conditions. Among current video traffic models, Autoregressive (AR) processes have been extensively used as good representation of variable bit rate video services, due basically to their simplicity and ease of computat...