58 citations · 104 across the 21 of their papers we have counts for
17 papers · 1 filter
A binary factor model
Luis E. Nieto-Barajas
The orthogonal factor model has been a very useful tool in uncovering covariance structures in a set of variables through a smaller set of underlying factors. This old model is sui…
The Dirichlet Process as sampling distribution
Luis E. Nieto-Barajas
The Dirichlet process (DP) is the most common bayesian nonparametric prior, however, its properties as sampling distribution have not been studied nor inference on its parameters.…
Modelling heavy tail data with bayesian nonparametric mixtures
Luis E. Nieto-Barajas
In the study of heavy tail data, several models have been introduced. If the interest is in the tail of the distribution, block maxima or excess over thresholds are the typical app…
Negative binomial models for development triangles of counts
Luis E. Nieto-Barajas, Rodrigo S. Targino
Prediction of outstanding claims has been done via nonparametric models (chain ladder), semiparametric models (overdispersed poisson) or fully parametric models. In this paper, we…
Graphical models with marginals in the exponential family
Luis E. Nieto-Barajas, Simón Lunagómez
Graphical models encode conditional independence statements of a multivariate distribution via a graph. Traditionally, the marginal distributions in a graphical model are assumed t…
Bayesian nonparametric mixtures of Archimedean copulas
Ruyi Pan, Luis E. Nieto-Barajas, Radu V. Craiu
Copula-based dependence modeling often relies on parametric formulations. This is mathematically convenient, but can be statistically inefficient when the parametric families are n…
Multivariate temporal dependence via mixtures of rotated copulas
Ruyi Pan, Luis E. Nieto-Barajas, Radu Craiu
Parametric copula families have been known to flexibly capture various dependence patterns, e.g., either positive or negative dependence in either the lower or upper tails of bivar…
Multivariate and regression models for directional data based on projected Pólya trees
Luis E. Nieto-Barajas
Projected distributions have proved to be useful in the study of circular and directional data. Although any multivariate distribution can be used to produce a projected model, the…
Generalised bayesian sample copula of order
Luis E. Nieto-Barajas, Ricardo Hoyos-Argüelles
In this work we propose a semiparametric bivariate copula whose density is defined by a piecewise constant function on disjoint squares. We obtain the maximum likelihood estimators…
A class of dependent Dirichlet processes via latent multinomial processes
Luis E. Nieto-Barajas
We describe a procedure to introduce general dependence structures on a set of Dirichlet processes. Dependence can be in one direction to define a time series or in two directions…
Optimal stratification of survival data via Bayesian nonparametric mixtures
Riccardo Corradin, Luis Enrique Nieto-Barajas, Bernardo Nipoti
The stratified proportional hazards model represents a simple solution to account for heterogeneity within the data while keeping the multiplicative effect on the hazard function.…
Bayesian nonparametric dynamic hazard rates in evolutionary life tables
Luis E. Nieto-Barajas
In the study of life tables the random variable of interest is usually assumed discrete since mortality rates are studied for integer ages. In dynamic life tables a time domain is…
Dependence on a collection of Poisson random variables
Luis E. Nieto-Barajas
We propose two novel ways of introducing dependence among Poisson counts through the use of latent variables in a three levels hierarchical model. Marginal distributions of the ran…
Projected Pólya Tree
Luis Nieto-Barajas, Gabriel Nuñez-Antonio
One way of defining probability distributions for circular variables (directions in two dimensions) is to radially project probability distributions, originally defined on $\mathbb…
Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures
Sarah Filippi, Chris C. Holmes, Luis E. Nieto-Barajas
In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accoun…
Characterising variation of nonparametric random probability measures using the Kullback-Leibler divergence
James Watson, Luis Nieto-Barajas, Chris Holmes
This work studies the variation in Kullback-Leibler divergence between random draws from some popular nonparametric processes and their baseline measure. In particular we focus on…
Modeling with Normalized Random Measure Mixture Models
Ernesto Barrios, Antonio Lijoi, Luis E. Nieto-Barajas +1
The Dirichlet process mixture model and more general mixtures based on discrete random probability measures have been shown to be flexible and accurate models for density estimatio…