9 papers
A Bayesian bivariate conditional Poisson regression for goal dependence in the English Premier League
Marcus Nolan, Wagner Barreto-Souza, Luiza S. C. Piancastelli +1
Understanding the relationship between home and away goal counts in football provides valuable insights into match-level dynamics. While the influence of home advantage is well-est…
Mixed Time Series Quasi-Likelihood Models for Uncovering Covid-19 Viral Load and Mortality Dynamics
Kejin Wu, Raanju R. Sundararajan, Michel F. C. Haddad +2
Accurate real-time monitoring of disease transmission is crucial for epidemic control, which has conventionally relied on reported cases or hospital admissions. Such metrics are fr…
A multivariate heavy-tailed integer-valued GARCH process with EM algorithm-based inference
Yuhyeong Jang, Raanju R. Sundararajan, Wagner Barreto-Souza
A new multivariate integer-valued Generalized AutoRegressive Conditional Heteroscedastic process based on a multivariate Poisson generalized inverse Gaussian distribution is propos…
Frequency Band Analysis of Nonstationary Multivariate Time Series
Raanju R. Sundararajan, Scott A. Bruce
Information from frequency bands in biomedical time series provides useful summaries of the observed signal. Many existing methods consider summaries of the time series obtained ov…
Student-t Stochastic Volatility Model With Composite Likelihood EM-Algorithm
Raanju R. Sundararajan, Wagner Barreto-Souza
A new robust stochastic volatility (SV) model having Student-t marginals is proposed. Our process is defined through a linear normal regression model driven by a latent gamma proce…
Change Point Detection in Nonstationary Sub-Hourly Wind Time Series
Sakitha Ariyarathne, Harsha Gangammanavar, Raanju R. Sundararajan
In this paper, we present a change point detection method for detecting change points in multivariate nonstationary wind speed time series. The change point method identifies chang…
Principal Component Analysis using Frequency Components of Multivariate Time Series
Raanju R. Sundararajan
Dimension reduction techniques for multivariate time series decompose the observed series into a few useful independent/orthogonal univariate components. We develop a spectral doma…
Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals
Raanju Ragavendar Sundararajan, Ron D. Frostig, Hernando Ombao
A common class of methods for analyzing of multivariate time series, stationary and nonstationary, decomposes the observed series into latent sources. Methods such as principal com…
Stationary subspace analysis of nonstationary covariance processes: eigenstructure description and testing
Raanju Ragavendar Sundararajan, Vladas Pipiras, Mohsen Pourahmadi
Stationary subspace analysis (SSA) searches for linear combinations of the components of nonstationary vector time series that are stationary. These linear combinations and their n…