1 citations · 4 across the 7 of their papers we have counts for
6 papers · 1 filter
Imputation of Missing Data Using Linear Gaussian Cluster-Weighted Modeling
Luis Alejandro Masmela-Caita, Thais Paiva Galletti, Marcos Oliveira Prates
Missing data theory deals with the statistical methods in the occurrence of missing data. Missing data occurs when some values are not stored or observed for variables of interest.…
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…
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…
Fast Bayesian inference of Block Nearest Neighbor Gaussian process for large data
Zaida C. Quiroz, Marcos O. Prates, Dipak K. Dey +1
This paper presents the development of a spatial block-Nearest Neighbor Gaussian process (block-NNGP) for location-referenced large spatial data. The key idea behind this approach…
Bayesian linear regression models with flexible error distributions
Nívea B. da Silva, Marcos O. Prates, Flávio B. Gonçalves
This work introduces a novel methodology based on finite mixtures of Student-t distributions to model the errors' distribution in linear regression models. The novelty lies on a pa…