Attributing to the flexibility in considering various types of observation error and model error, data assimilation has been increasingly applied to dynamically improve soil moisture modeling in many hydrological practices. ...

In this paper we deal with the issue of performing accurate testing inference on a scalar parameter of interest in structural errors-in-variables models. The error terms are allowed to follow a multivariate distribution ...

In this paper we consider structural measurement error models within the elliptical family of distributions. We consider dependent and independent elliptical models, each of which requires special treatment methodology. ...

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error ...

In regression models in which a covariate is measured with error, it is common to use structures that correlate the observed covariate with the true non-observed covariate. Such structures are usually additive or ...

The main goal of this paper is to study inference in an heteroskedastic calibration model. We embrace a multivariate structural model with known diagonal covariance error matrices, which is a common setup when different ...

In this article we provide a Bayesian analysis for dependent elliptical measurement error models considering nondifferential and differential errors. In both cases we compute posterior distributions for structural parameters ...

The usual regression model fits data under the assumption that the explanatory variable is measured without error. However, in many situations the explanatory variable is observed with measurement errors. In these cases, ...

In this paper, we deal with inference about the structural parameters in a heteroscedastic functional measurement error models under the normal distribution assumption. Given a minimal sufficient statistic for the incidental ...