1 citations · 4 across the 6 of their papers we have counts for
7 papers
Alleviating Spatial Confounding in Spatial Frailty Models
Douglas Roberto Mesquita Azevedo, Marcos Oliveira Prates, Dipankar Bandyopadhyay
Spatial confounding is how is called the confounding between fixed and spatial random effects. It has been widely studied and it gained attention in the past years in the spatial s…
A robust nonlinear mixed-effects model for COVID-19 deaths data
Fernanda L. Schumacher, Clecio S. Ferreira, Marcos O. Prates +2
The analysis of complex longitudinal data such as COVID-19 deaths is challenging due to several inherent features: (i) Similarly-shaped profiles with different decay patterns; (ii)…
Heckman selection-t model: parameter estimation via the EM-algorithm
Victor H. Lachos Davila, Marcos O. Prates, Dipak K. Dey
Heckman selection model is perhaps the most popular econometric model in the analysis of data with sample selection. The analyses of this model are based on the normality assumptio…
Non-Separable Spatio-temporal Models via Transformed Gaussian Markov Random Fields
Douglas R. M. Azevedo, Marcos O. Prates, Michael R. Willig
Models that capture the spatial and temporal dynamics are applicable in many science fields. Non-separable spatio-temporal models were introduced in the literature to capture these…
Objective Bayesian analysis for spatial Student-t regression models
Jose A. Ordoñez, Marcos O. Prates, Larissa A. Matos +1
The choice of the prior distribution is a key aspect of Bayesian analysis. For the spatial regression setting a subjective prior choice for the parameters may not be trivial, from…
Dynamic Time Scan Forecasting
Marcelo Azevedo Costa, Leandro Brioschi Mineti, Marcos Oliveira Prates +1
The dynamic time scan forecasting method relies on the premise that the most important pattern in a time series precedes the forecasting window, i.e., the last observed values. Thu…