Absfrucf -A universal nearly efficient estimator is proposed for the first order autoregressive (AR) model where the probability distribution of the driving noise is unknown. The proposed estimator has an intuitively appealing relation to universal data compression and to universal tests for randomness. Index Terms -asymptotically efficient estimation, universal estimation, Cramer-Rao bound, Fi...

We present a methodology for generating probabilistic predictions for the Disturbance Storm Time (Dst) geomagnetic activity index. We focus on the One Step Ahead prediction task and use the OMNI hourly resolution data to build our models. Our proposed methodology is based on the technique of Gaussian Process Regression. Within this framework we develop two models; Gaussian Process Autoregressiv...

The first–order integer–valued autoregressive (INAR(1)) process is investigated, where the autoregressive coefficient is close to one. It is shown that the limiting distribution of the conditional least–squares estimator for this coefficient is normal and, in contrast to the familiar AR(1) process, the rate of convergence is n. Finally, the nearly critical Galton–Watson process with unobservabl...

the classical method of process capability analysis necessarily assumes that collected data are independent; nonetheless, some processes such as biological and chemical processes are autocorrelated and violate the independency assumption. many processes exhibit a certain degree of correlation and can be treated by autoregressive models among which the autoregressive model of order one (ar (1)) ...

Multi-rate digital processing [1] is today a well-established topic, extensively applied in communications, image and audio industry and other areas, for signal coding, adaptive or statistical processing etc. A special class of discrete random processes [2] are those obtained by passing white-noise through a linear digital filter—called Moving-Average (MA) for an Finite-Impulse-Response (FIR) f...

Image anomaly detection is the process of extracting a small number of clustered pixels which are different from the background. The type of image, its characteristics and the type of anomalies depend on the application at hand. In this paper, we introduce a new statistical model called noncausal autoregressive–autoregressive conditional heteroscedasticity (AR-ARCH) model for background in sona...

The classical method of process capability analysis necessarily assumes that collected data are independent; nonetheless, some processes such as biological and chemical processes are autocorrelated and violate the independency assumption. Many processes exhibit a certain degree of correlation and can be treated by autoregressive models, among which the autoregressive model of order one (AR (1))...

Abstract The class of autoregressive (AR) processes is extensively used to model temporal dependence in observed time series. Such models are easily available and routinely fitted using freely statistical software like . A potential problem that commonly applied estimators for the coefficients AR severely biased when series short. This paper studies finite-sample properties well-known stationar...

In this paper a time-frequency estimator for enhancement of noisy speech signals in the DFT domain is introduced. This estimator is based on modeling the time-varying correlation of the temporal trajectories of the short time (ST) DFT components of the noisy speech signal using autoregressive (AR) models. The timevarying trajectory of the DFT components of speech in each channel is modeled by a...