Several autoregressive (AR) and autoregressive moving average (ARMA) parametric spectral estimators were evaluated for use in tissue strain estimation. Using both 1-D simulations and in vitro phantom experiments, the performance of these parametric spectral strain estimators were compared against both a nonparametric discrete Fourier transform (DFT) spectral strain estimator and a coherent elas...

Parametric modeling of third-order cumulant sequences has assumed importance recently because of the applications of the bispectrum. Approaches have been developed for autoregressive modeling of random processes using third-order cumulants, and these are based on solving linear equations which a re necessary but not sufficient conditions for matching samples of the cumulant sequence of the mode...

Traffic estimation and modeling has been a crucial issue in many research areas of communication networks. For example, intermediate routers in networks estimate the rate of packet flows via queue size information in order to maximize throughput and provide fairness between flows. Also, many senders in the end system such as multimedia applications estimate end-to-end delay between a sender and...

In this paper, we propose two new implementations of the LMS/Newton algorithm for efficient realization of long adaptive filters. We assume that the input sequence to the adaptive filter can be modeled as an autoregressive (AR) process whose order may be kept much lower than the adaptive filter length. The two algorithms differ in their structural complexity. The first algorithm, which will be ...

In this paper we propose a new class of nonlinear time series models, the threshold variable driven switching autoregressive models. It is a hierarchical model that combines two important nonlinear time series models, the threshold autoregressive (AR) models and the random switching AR models. The underlying time series process switches between two (or more) different linear models. The switchi...

A new theoretical approximation for expectation of the prediction error is derived using the same-realization predictions. This approximation is derived for the case that the Least-Squares-Forward (LSF) method (the covariance method) is used for estimating the parameters of the autoregressive (AR) model. This result is used for obtaining modified versions of the AR order selection criteria FPE ...

This work deals with the limiting distribution of the least squares estimators of the coefficients ar of an explosive periodic autoregressive of order 1 (PAR(1)) time series Xr = arXr−1+ur when the innovation {uk} is strongly mixing. More precisely {ar} is a periodic sequence of real numbers with period P > 0 and such that ∏P r=1 |ar| > 1. The time series {ur} is periodically distributed with t...

It is proposed to jointly estimate the parameters of nonGaussian autoregressive (AR) processes in a Bayesian context using the Gibbs sampler. Using the Markov chains produced by the sampler an approximation to the vector MAP estimator is implemented. The results reported here used AR(4) models driven by noise sequences where each sample is iid as a two component Gaussian sum mixture. The result...

Autoregressive (AR) extrapolation of travel-time data is tested using several synthetic tomography examples using a cross-borehole geometry. Earlier studies have shown crossborehole tomography using travel-times can have reduced resolution because of the limitations on ray coverage. We apply AR extrapolation to partial travel-time data and then compare the tomographic inversions using the full ...

Differences between spectral estimation techniques using fast Fourier transform (FFT), autoregressive modelling (AR) and Lomp-Scargle periodogram (LSP) with respect to the impact of metronomic breathing on sympathovagal balance were investigated by calculating heart rate variability frequency domain LF/HF ratio for 25 subjects under paced respiration. All techniques showed a significant increas...