10 citations · 10 across the 9 of their papers we have counts for
11 papers
A spatiotemporal negative binomial model with dynamic dispersion: An application to Tuberculosis infections
Rodrigo B. Silva, Luiza S. C. Piancastelli, Wagner Barreto-Souza
Tuberculosis (TB) remains a critical public health concern in Brazil, characterized by pronounced spatial heterogeneity and fluctuating temporal volatility. In this paper, we study…
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…
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…
Clustered Mallows Model
Luiza S. C. Piancastelli, Nial Friel
Rankings are a type of preference elicitation that arise in experiments where assessors arrange items, for example, in decreasing order of utility. Orderings of n items labelled {1…
Time-Varying Dispersion Integer-Valued GARCH Models
Wagner Barreto-Souza, Luiza S. C. Piancastelli, Konstantinos Fokianos +1
We propose a general class of INteger-valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing time-varying mean and dispersion parameters, w…
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…
Generalized inverse-Gaussian frailty models with application to TARGET neuroblastoma data
Luiza Sette Câmara Piancastelli, Wagner Barreto-Souza, Vinícius Diniz Mayrink
A new class of survival frailty models based on the Generalized Inverse-Gaussian (GIG) distributions is proposed. We show that the GIG frailty models are flexible and mathematicall…