Regression data often suffer from the so-called Berkson measurement error which contaminates the design variables. Conventional nonparametric approaches to this errors-in-variables problem usually require rather strong conditions on the support of the design density and that of the contaminated...

Mallows' Cp statistic is widely used for selecting multivariate linear regression models. It can be considered to be an estimator of a risk function based on an expected standardized mean square error of prediction. An unbiased Cp criterion for selecting multivariate linear regression models has...

A general approach for developing distribution free tests for general linear models based on simplicial depth is applied to multiple regression. The tests are based on the asymptotic distribution of the simplicial regression depth, which depends only on the distribution law of the vector product...

Data are often affected by uncertainty. Uncertainty is usually referred to as randomness. Nonetheless, other sources of uncertainty may occur. In particular, the empirical information may also be affected by imprecision. Also in these cases it can be fruitful to analyze the underlying structure...

We consider a panel data semiparametric partially linear regression model with an unknown parameter vector for the linear parametric component, an unknown nonparametric function for the nonlinear component, and a one-way error component structure which allows unequal error variances...

We equip the polytope of nxn Markov matrices with the normalized trace of the Lebesgue measure of . This probability space provides random Markov matrices, with i.i.d. rows following the Dirichlet distribution of mean (1/n,...,1/n). We show that if is such a random matrix, then the empirical...

We investigate the estimation problem of parameters in a two-sample semiparametric model. Specifically, let X1,...,Xn be a sample from a population with distribution function G and density function g. Independent of the Xi's, let Z1,...,Zm be another random sample with distribution function H...

We propose a two-sample adjusted empirical likelihood (AEL) to construct confidence regions for the difference of two d-dimensional population means. This method eliminates the non-definition of the usual two-sample empirical likelihood (EL) and is shown to be Bartlett correctable. We further...

For independently distributed observables: Xi~N([theta]i,[sigma]2),i=1,...,p, we consider estimating the vector [theta]=([theta]1,...,[theta]p)' with loss ||d-[theta]||2 under the constraint , with known [tau]1,...,[tau]p,[sigma]2,m. In comparing the risk performance of Bayesian estimators...