19 documents found, page 1 of 2

Time Series of Counts under Censoring: A Bayesian Approach

Novel Features for Time Series Analysis: A Complex Networks Approach

Multivariate and Multiscale Complexity of Long-Range Correlated Cardiovascular ...

Assessing the dynamical complexity of biological time series represents an important topic with potential applications ranging from the characterization of physiological states and pathological conditions to the calculation of diagnostic parameters. In particular, cardiovascular time series exhibit a variability produced by different physiological control mechanisms coupled with each other, which take into acco...

Inference for bivariate integer-valued moving average models based on binomial ...

Time series of (small) counts are common in practice and appear in a wide variety of fields. In the last three decades, several models that explicitly account for the discreteness of the data have been proposed in the literature. However, for multivariate time series of counts several difficulties arise and the literature is not so detailed. This work considers Bivariate INteger-valued Moving Average, BINMA, mo...

Modelling Overdispersion with Integer-Valued Moving Average Processes

A new first-order integer-valued moving average, INMA(1), model based on the negative binomial thinning operation defined by Risti et al. [21] is proposed and characterized. It is shown that this model has negative binomial (NB) marginal distribution when the innovations follow an NB distribution and therefore it can be used in situations where the data present overdispersion. Additionally, this model is extend...

Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series

This work investigates outlier detection and modelling in non-Gaussian autoregressive time series models with margins in the class of a convolution closed parametric family. This framework allows for a wide variety of models for count and positive data types. The article investigates additive outliers which do not enter the dynamics of the process but whose presence may adversely influence statistical inference...

Dynamic Principal Component Analysis

Multiscale information storage of linear long-range correlated stochastic proce...

Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochas...

Wavelet-Based Detection of Outliers in Poisson INAR(1) Time Series

The presence of outliers or discrepant observations has a negative impact in time series modelling. This paper considers the problem of detecting outliers, additive or innovational, single, multiple or in patches, in count time series modelled by first-order Poisson integer-valued autoregressive, PoINAR(1), models. To address this problem, two wavelet-based approaches that allow the identification of the time p...

ARFIMA-GARCH modeling of HRV: Clinical application in acute brain injury

In the last decade, several HRV based novel methodologies for describing and assessing heart rate dynamics have been proposed in the literature with the aim of risk assessment. Such methodologies attempt to describe the non-linear and complex characteristics of HRV, and hereby the focus is in two of these characteristics, namely long memory and heteroscedasticity with variance clustering. The ARFIMA-GARCH model...