6 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
MaÃra 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…
A Unified Spatiotemporal Framework for Modeling Censored and Missing Areal Responses
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. The method introduces a flexible random effect that combines the spatial de…
Parametric modal regression for right-censored positive responses
Christian E. Galarza, VÃctor 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…
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…