308 citations · 933 across the 30 of their papers we have counts for
34 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…
Generalised Linear Models Driven by Latent Processes: Asymptotic Theory and Applications
Wagner Barreto-Souza, Ngai Hang Chan
This paper introduces a class of generalised linear models (GLMs) driven by latent processes for modelling count, real-valued, binary, and positive continuous time series. Extendin…
Generalised Exponential Kernels for Nonparametric Density Estimation
Laura M. Craig, Wagner Barreto-Souza
This paper introduces a novel kernel density estimator (KDE) based on the generalised exponential (GE) distribution, designed specifically for positive continuous data. The propose…
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…
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…
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…
Modified Galton-Watson processes with immigration under an alternative offspring mechanism
Wagner Barreto-Souza, Sokol Ndreca, Rodrigo B. Silva +1
We propose a novel class of count time series models alternative to the classic Galton-Watson process with immigration (GWI) and Bernoulli offspring. A new offspring mechanism is d…
Poisson-Birnbaum-Saunders Regression Model for Clustered Count Data
Jussiane Nader Gonçalves, Wagner Barreto-Souza, Hernando Ombao
The premise of independence among subjects in the same cluster/group often fails in practice, and models that rely on such untenable assumption can produce misleading results. To o…
Nearly Unstable Integer-Valued ARCH Process and Unit Root Testing
Wagner Barreto-Souza, Ngai Hang Chan
This paper introduces a Nearly Unstable INteger-valued AutoRegressive Conditional Heteroskedasticity (NU-INARCH) process for dealing with count time series data. It is proved that…
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…
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…
Fractional Poisson random sum and its associated normal variance mixture
Gabriela Oliveira, Wagner Barreto-Souza, Roger W. C. Silva
In this work, we study the partial sums of independent and identically distributed random variables with the number of terms following a fractional Poisson (FP) distribution. The F…
A Generalized Heckman Model With Varying Sample Selection Bias and Dispersion Parameters
Fernando de S. Bastos, Wagner Barreto-Souza, Marc G. Genton
Many proposals have emerged as alternatives to the Heckman selection model, mainly to address the non-robustness of its normal assumption. The 2001 Medical Expenditure Panel Survey…
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…
Convergence and inference for mixed Poisson random sums
Wagner Barreto-Souza, Gabriela Oliveira, Roger W. C. Silva
In this paper we obtain the limit distribution for partial sums with a random number of terms following a class of mixed Poisson distributions. The resulting weak limit is a mixing…
Semiparametric time series models driven by latent factor
Gisele O. Maia, Wagner Barreto-Souza, Fernando S. Bastos +1
We introduce a class of semiparametric time series models by assuming a quasi-likelihood approach driven by a latent factor process. More specifically, given the latent process, we…
Integer-valued autoregressive process with flexible marginal and innovation distributions
Matheus B. Guerrero, Wagner Barreto-Souza, Hernando Ombao
INteger Auto-Regressive (INAR) processes are usually defined by specifying the innovations and the operator, which often leads to difficulties in deriving marginal properties of th…
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…
Bessel regression model: Robustness to analyze bounded data
Wagner Barreto-Souza, Vinícius D. Mayrink, Alexandre B. Simas
Beta regression has been extensively used by statisticians and practitioners to model bounded continuous data and there is no strong and similar competitor having its main features…
Beta and Kumaraswamy distributions as non-nested hypotheses in the modeling of continuous bounded data
Rodrigo B. Silva, Wagner Barreto-Souza
Nowadays, beta and Kumaraswamy distributions are the most popular models to fit continuous bounded data. These models present some characteristics in common and to select one of th…
A skew true INAR(1) process with application
Wagner Barreto-Souza, Marcelo Bourguignon
Integer-valued time series models have been a recurrent theme considered in many papers in the last three decades, but only a few of them have dealt with models on (tha…