7 papers
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…
Time Series Analysis of Rankings: A GARCH-Type Approach
Luiza Piancastelli, Wagner Barreto-Souza
Ranking data are frequently obtained nowadays but there are still scarce methods for treating these data when temporally observed. The present paper contributes to this topic by pr…
Granger Causality for Mixed Time Series Generalized Linear Models: A Case Study on Multimodal Brain Connectivity
Luiza S. C. Piancastelli, Wagner Barreto-Souza, Norbert J. Fortin +2
This paper is motivated by studies in neuroscience experiments to understand interactions between nodes in a brain network using different types of data modalities that capture dif…
Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly-Intractable Bayesian Inference
Luiza S. C. Piancastelli, Nial Friel, Wagner Barreto-Souza +1
In this paper, a multivariate count distribution with Conway-Maxwell (COM)-Poisson marginals is proposed. To do this, we develop a modification of the Sarmanov method for construct…
A Bayesian latent allocation model for clustering compositional data with application to the Great Barrier Reef
Luiza Piancastelli, Nial Friel, Julie Vercelloni +2
Relative abundance is a common metric to estimate the composition of species in ecological surveys reflecting patterns of commonness and rarity of biological assemblages. Measureme…
Flexible Bivariate INGARCH Process With a Broad Range of Contemporaneous Correlation
Luiza S. C. Piancastelli, Wagner Barreto-Souza, Hernando Ombao
We propose a novel flexible bivariate conditional Poisson (BCP) INteger-valued Generalized AutoRegressive Conditional Heteroscedastic (INGARCH) model for correlated count time seri…