This paper presents a two-part fast recursive algorithm for ARMA modeling. The algorithm first obtains estimates of the p autoregressive coefficients from a set of p extended Yule-Walker equations. An exact recursive lattice algorithm for this estimator is then derived. The 4 + 1 numerator spectrum coefficients are then obtained by using one of the output data sequences of this lattice algorith...

Background: Migraine headache without aura is the most common type of migraine especially among pediatric patients. It has always been a great challenge of migraine diagnosis using quantitative electroencephalography measurements through feature classification. It has been proven that different feature extraction and classification methods vary in terms of performance regarding detection and di...

Vector autoregressive models are increasingly being used in the analysis of relationships within and between financial markets. In such models, there are circumstances that require zero entries in the coefficient matrices. Such circumstances can be particularly relevant in the context of markets with special characteristics, such as emerging economies. This paper shows that a direct extension o...

In this paper, we study the autoregressive (AR) model with normal inverse Gaussian (NIG) innovations. The NIG distribution is semi heavy-tailed and helpful in capturing extreme observations present data. expectation-maximization (EM) algorithm used to estimate parameters of considered AR(p) model. efficacy estimation procedure shown on simulated data for AR(2) AR(1) models. A comparative presen...

In this paper a method of design minimal phase digital filters with arbitrary amplitude characteristics is presented. The filters designed using proposed method can be directly implemented in the hardware. Filter coefficients obtained using proposed method are immune on rounding error. Because these coefficients are created in required bit word length (based on numerical format which is used in...

The LASSO (Tibshirani, J R Stat Soc Ser B 58(1):267–288, 1996, [30]) and the adaptive LASSO (Zou, J Am Stat Assoc 101:1418–1429, 2006, [37]) are popular in regression analysis for their advantage of simultaneous variable selection and parameter estimation, and also have been applied to autoregressive time series models. We propose the doubly adaptive LASSO (daLASSO), or PLAC-weighted adaptive L...

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...

There is an array of numerical techniques available to estimate the period of circadian and other biological rhythms. Criteria for choosing a method include accuracy of period measurement, resolution of signal embedded in noise or of multiple periodicities, and sensitivity to the presence of weak rhythms and robustness in the presence of stochastic noise. Maximum Entropy Spectral Analysis (MESA...

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and ...

Electroencephalography is an essential clinical tool for the evaluation and treatment of neurophysiologic disorders related to epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important element in the diagnosis of e...