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...

Summary In this paper, we develop a systematic theory for high-dimensional analysis of variance in multivariate linear regression, where the dimension and number coefficients can both grow with sample size. We propose new U-type statistic to test hypotheses establish Gaussian approximation result under fairly mild moment assumptions. Our general framework be used deal classical one-way variance...

We review Hildreth's algorithm for computing the least squares regression subject to inequality constraints and Dykstra's generalization. We provide a geometric proof of convergence and several ehancements to the algorithm and generalize the application of the algorithm from convex cones to convex sets.

Bootstrapping is a computer-intensive statistical method which treats the data set as a population and draws samples from it with replacement. This resampling method has wide application areas especially in mathematically intractable problems. In this study, it is used to obtain the empirical distributions of the parameters to determine whether they are statistically significant or not in a spe...

We consider panel count data which are frequently obtained in prospective studies involving recurrent events that are only detected and recorded at periodic assessment times. The data take the form of counts of the cumulative number of events detected at each inspection time, along with explanatory covariates. Examples arise in diverse areas such as epidemiological studies, medical follow-up st...

In this paper, we consider a generalized multivariate regression problem where the responses are monotonic functions of linear transformations of predictors. We propose a semi-parametric algorithm based on the ordering of the responses which is invariant to the functional form of the transformation function. We prove that our algorithm, which maximizes the rank correlation of responses and line...

The item count technique is a survey methodology that is designed to elicit respondents’ truthful answers to sensitive questions such as racial prejudice and drug use. The method is also known as the list experiment or the unmatched count technique and is an alternative to the commonly used randomized response method. In this article, I propose new nonlinear least squares and maximum likelihood...

MOTIVATION It is widely acknowledged that microarray data are subject to high noise levels and results are often platform dependent. Therefore, microarray experiments should be replicated several times and in several laboratories before the results can be relied upon. To make the best use of such extensive datasets, methods for microarray data fusion are required. Ideally, the fused data should...