21 citations · 53 across the 19 of their papers we have counts for
20 papers
Semiparametric robust mixture of experts based on nonparametric maximum likelihood
Sangkon Oh, Victor H. Lachos, Byungtae Seo
The mixture of experts (MoE) model provides a flexible approach for modeling heterogeneous regression relationships by allowing covariate-dependent mixing through a gating network,…
Extensions in Semiparametric Geostatistical Models
Maíra Soalheiro, Marcos Oliveira Prates, Victor Hugo Lachos +1
In spatial statistics, the incorrect selection of an appropriate covariance function may lead to inference errors and confidence underestimation. Motivated by such restrictions, we…
Multiple Heckman Selection Model
Heeju Lim, Carlos A. R. Diniz, Ofer Harel +1
We introduce a novel matrix-variate extension of the Heckman selection model to accommodate multiple outcomes, providing a flexible and natural generalization of classical selectio…
Parametric modal regression for right-censored positive responses
Christian E. Galarza, Víctor H. Lachos
We present a unified parametric framework for modal regression applicable to continuous positive distributions, with explicit support for right-censored observations. The key contr…
Scalable Bayesian Spatiotemporal Tensor Modeling for Censored and Missing Areal Data
Jose A. Ordoñez, Tsung-I Lin, Victor H. Lachos +1
We propose a new Bayesian approach for spatiotemporal areal data with censored and missing observations, where the response is represented as a tensor indexed by spatial units and…
Bayesian analysis of flexible Heckman selection models using Hamiltonian Monte Carlo
Heeju Lim, Victor E. Lachos, Victor H. Lachos
The Heckman selection model is widely used in econometric analysis and other social sciences to address sample selection bias in data modeling. A common assumption in Heckman selec…
Heckman Selection Contaminated Normal Model
Heeju Lim, Jose Alejandro Ordonez, Victor H. Lachos +1
The Heckman selection model is one of the most well-renounced econometric models in the analysis of data with sample selection. This model is designed to rectify sample selection b…
The use of the EM algorithm for regularization problems in high-dimensional linear mixed-effects models
Daniela C. R. Oliveira, Fernanda L. Schumacher, Victor H. Lachos
The EM algorithm is a popular tool for maximum likelihood estimation but has not been used much for high-dimensional regularization problems in linear mixed-effects models. In this…
Spatial Censored Regression Models in R: The CensSpatial package
Jose A. Ordonez, Christian E. Galarza, Victor H. Lachos
CensSpatial is an R package for analyzing spatial censored data through linear models. It offers a set of tools for simulating, estimating, making predictions, and performing local…
On moments of folded and doubly truncated multivariate extended skew-normal distributions
Christian E. Galarza, Larissa A. Matos, Dipak K. Dey +1
This paper develops recurrence relations for integrals that relate the density of multivariate extended skew-normal (ESN) distribution, including the well-known skew-normal (SN) di…
Finite mixture modeling of censored and missing data using the multivariate skew-normal distribution
Francisco H. C. de Alencar, Christian E. Galarza, Larissa A. Matos +1
Finite mixture models have been widely used to model and analyze data from a heterogeneous populations. Moreover, data of this kind can be missing or subject to some upper and/or l…
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)…
Approximate inferences for nonlinear mixed effects models with scale mixtures of skew-normal distributions
Fernanda L. Schumacher, Dipak K. Dey, Victor H. Lachos
Nonlinear mixed effects models have received a great deal of attention in the statistical literature in recent years because of their flexibility in handling longitudinal studies,…
Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew- distribution
Christian E. Galarza, Larissa A. Matos, Victor H. Lachos
In this paper, we compute doubly truncated moments for the selection elliptical (SE) class of distributions, which includes some multivariate asymmetric versions of well-known elli…
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…
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…
Scale mixture of skew-normal linear mixed models with within-subject serial dependence
Fernanda L. Schumacher, Victor H. Lachos, Larissa A. Matos
In longitudinal studies, repeated measures are collected over time and hence they tend to be serially correlated. In this paper we consider an extension of skew-normal/independent…
An Extended Poisson Family of Life Distribution: A Unified Approach in Competitive and Complementary Risks
Pedro L. Ramos, Dipak K. Dey, Francisco Louzada +1
In this paper, we introduce a new approach to generate flexible parametric families of distributions. These models arise on competitive and complementary risks scenario, in which t…
Case-Deletion Diagnostics for Quantile Regression Using the Asymmetric Laplace Distribution
Luis E. Benites, Víctor H. Lachos, Filidor E. Vilca
To make inferences about the shape of a population distribution, the widely popular mean regression model, for example, is inadequate if the distribution is not approximately Gauss…
Robust Bayesian model selection for heavy-tailed linear regression using finite mixtures
Flávio B Gonçalves, Marcos O. Prates, Victor H. Lachos
In this paper we present a novel methodology to perform Bayesian model selection in linear models with heavy-tailed distributions. We consider a finite mixture of distributions to…