49 citations · 70 across the 8 of their papers we have counts for
9 papers
The risks of dichotomising ordinal outcomes: A spatial analysis of self-rated health in Western Europe
Miguel Ángel Beltrán-Sánchez, Miguel Ángel Martínez-Beneito, Terje Eikemo +2
Survey responses are often measured using ordered response categories. In the European Social Survey, self-rated health is measured on a five-point scale from very good to very bad…
A multivariate spatial model for ordinal survey-based data
Miguel Ángel Beltrán-Sánchez, Miguel Ángel Martínez-Beneito, Ana Corberán-Vallet
Health surveys provide valuable information for monitoring population health, identifying risk factors and informing public health policies. Most of the questions included are code…
A proposal for homoscedastic modelling with conditional auto-regressive distributions
Miguel A. Martinez-Beneito, Aritz Adín, Tomás Goicoa +1
Conditional auto-regressive (CAR) distributions are widely used to induce spatial dependence in the geographic analysis of areal data. These distributions establish multivariate de…
Bayesian modeling of spatial ordinal data from health surveys
Miguel Ángel Beltrán Sánchez, Miguel Ángel Martínez Beneito, Ana Corberán Vallet
Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. Thes…
Taking advantage of sampling deisgns in Bayesian spatial small ares survey studies
Carlos Vergara-Hernández, Marc Marí-DellOlmo, Laura Oliveras +1
Spatial small area estimation models have become very popular in some contexts, such as disease mapping. Data in disease mapping studies are exhaustive, that is, the available data…
Bayesian Multivariate Spatial Models for Lattice Data with INLA
Francisco Palmi-Perales, Virgilio Gomez-Rubio, Miguel A. Martinez-Beneito
The INLAMSM package for the R programming language provides a collection of multivariate spatial models for lattice data that can be used with package INLA for Bayesian inference.…
Bayesian variable selection in high dimensional problems without assumptions on prior model probabilities
James O. Berger, Gonzalo Garcia-Donato, Miguel A. Martinez-Beneito +1
We consider the problem of variable selection in linear models when , the number of potential regressors, may exceed (and perhaps substantially) the sample size (which is po…
A Bayesian Joinpoint regression model with an unknown number of break-points
Miguel A. Martinez-Beneito, Gonzalo García-Donato, Diego Salmerón
Joinpoint regression is used to determine the number of segments needed to adequately explain the relationship between two variables. This methodology can be widely applied to real…
Inferences in Bayesian variable selection problems with large model spaces
Gonzalo Garcia-Donato, Miguel Angel Martinez-Beneito
An important aspect of Bayesian model selection is how to deal with huge model spaces, since exhaustive enumeration of all the models entertained is unfeasible and inferences have…