In this article, we consider a non-parametric Bayesian approach to multivariate quantile regression. The proposed involves modeling of related conditional distributions response vector given the covariates using Dependent Dirichlet Process (DDP) prior. DDP is used introduce dependence across covariates. flexible covariate-dependent mixture Gaussian kernels gives rise an induced posterior for de...

This paper considers identi…cation and estimation of a nonparametric regression model with an unobserved discrete covariate. The sample consists of a dependent variable and a set of covariates, one of which is discrete and arbitrarily correlates with the unobserved covariate. The observed discrete covariate has the same support as the unobserved covariate, and can be interpreted as a proxy or m...

Missing outcomes are a commonly occurring problem for cluster randomised trials, which can lead to biased and inefficient inference if ignored or handled inappropriately. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. In this study, we assessed the performance of unadjusted cluster-level analysis, baseline covariate-adjusted cluster-level anal...

Separate Cox analyses of all cause-specific hazards are the standard technique of choice to study the effect of a covariate in competing risks, but a synopsis of these results in terms of cumulative event probabilities is challenging. This difficulty has led to the development of the proportional subdistribution hazards model. If the covariate is known at baseline, the model allows for a summar...

We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of a specific nonparametric model, but can be applied to all models that can be represented using completely random measures. Several existing dependent random measures can be seen as specific cases of this framework. In...

Markov-switching models with covariate-dependent transition functions that are subject to exogenous discrete stochastic changes considered. These associated simultaneous in the covariance structure of observable variables. Simulation experiments carried out assess quality large-sample approximations distributions maximum-likelihood estimator and related statistics such a model, examine implicat...