Spatial autoregressive combined (SAC) model has been widely studied in the literature for analysis of spatial data various areas such as geography, economics, demography, regional sciences. This is a linear with scalar response, explanatory variables and which allows interactions dependent variable disturbances. In this work we extend modeling approach from to functional covariate. The paramete...

The definition of vectors of dependent random probability measures is a topic of interest in applications to Bayesian statistics. They, indeed, represent dependent nonparametric prior distributions that are useful for modelling observables for which specific covariate values are known. In this paper we propose a vector of two-parameter Poisson-Dirichlet processes. It is well-known that each com...

This work introduces a Bayesian semi-parametric approach for dealing with regression models where the covariate is measured with error. The main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in the dependent and independent situations. Conditional posterior distributions are imp...

A computationally efficient landmark-dependent hierarchical beta process is developed as a prior for data with associated covariates. The landmarks define local regions in the covariate space where feature usages are likely to be similar. The landmark locations are learned, to which the data are linked through normalized kernels. The proposed model is well suited for local latent feature discov...

Conditional copulas are flexible statistical tools that couple joint conditional and marginal conditional distributions. In a linear regression setting with more than one covariate and two dependent outcomes, we consider additive models for studying the dependence between covariates and the copula parameter. We examine the computation and model selection tools needed for Bayesian inference. The...

A nonparametric analysis of time-dependent covariate effects on failures determined by a regression function β0(t) in Cox’s regression model based on case-cohort sampling design is developed. The analysis is carried out through maximizing appropriate penalized pseudolikelihoods. Weak uniform consistency and pointwise asymptotic normality of the resulting estimators are investigated under regula...