Background and Aim: The study objective was to assess chaotic global metrics in malnourished children following power spectral manipulations. Methods: We evaluated the complexity of heart rate (HR) variability (HRV) subjects via six spectra (Welch, multi-taper method (MTM), Burg, covariance, Yule-Walker, periodogram) then, when adjusted by MTM parameters, for further refinement. Seventy were sp...

Time series data are often subject to measurement error, usually the result of needing to estimate the variable of interest. While it is often reasonable to assume the measurement error is additive, that is, the estimator is conditionally unbiased for the missing true value, the measurement error variances often vary as a result of changes in the population/process over time and/or changes in s...

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cere...

This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous predictors are based theoretical models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based predictor machine learning (ML)-based using the channels spatial model (SCM), has been adopted in 3GPP standard ye...

This paper describes a method for estimating the parameters of an autoregressive (AR) process from a nite number of noisy measurements. The method uses a modi ed set of YuleWalker equations that lead to a quadratic eigenvalue problem which when solved, gives estimates of the AR parameters and the measurement noise variance. Version 2, submitted to IEEE Transactions on Signal Processing January ...

Time series of counts have been the focus of attention due to their importance in several areas of knowledge. The usual stochastic processes assume continuous marginal distributions and therefore are not suitable for modeling series of counts. Thus arises the need to investigate methodologies for time series with discrete marginal distributions. In particular, the study of new models and the be...

We are interested in estimation of stationary GARCH models. In simulation studies, we assess the performance of the maximum likelihood estimator and Yule-Walker estimator of the GARCH (1, 1) model. Finally we attempt to fit the dynamics of daily stock returns on Nordea by a GARCH model.

The innovations algorithm can be used to obtain parameter estimates for periodically stationary time series models. In this paper we compute the asymptotic distribution for these estimates in the case where the innovations have a finite fourth moment. These asymptotic results are useful to determine which model parameters are significant. In the process, we also develop asymptotics for the Yule...

An algorithm for recursive Frisch scheme system identification of linear single-input single-output errors-in-variables systems is developed. For the update of the estimated model parameters, a recursive bias-compensating least squares algorithm, which is based on the wellknown recursive least squares technique, is considered. The estimate of the output measurement noise variance is determined ...

In this paper a Levinson-like algorithm is derived for solving symmetric positive definite semiseparable plus diagonal systems of equations. In a first part we solve a Yule-Walker-like system of equations. Based on this O(n) solver an algorithm for a general right-hand side is derived. The new method has a linear complexity and takes 19n − 13 operations. The relation between the algorithm and a...