VeranstaltungsortN.2.01Veranstalter Institut für MathematikBeschreibungTitel:Minimization based formulations of the EIT problemwith the complete electrode modelKurzfassung:One of the recent approaches to solving inverse problems is to use the all-atonce formulation where both the state and the parameter are considered asunknowns. The advantage of this method is to avoid using the parameterto-state map, which is usually difficult to determine in real problems andsometimes leads to strictly restrictive conditions. In this talk, we regularizethe electrical impedance tomography (EIT) problem with the complete electrode model (CEM) in the plane. The regularization method is to formulateour problem as a minimization problem, which is a generalization of theall-at-once formulation. The new one is to use CEM, a widely used practical model. Additionally, we also discuss implementing using Matlab. Keywords. inverse problems, regularization, minimization based formulations,EIT problem, complete electrode model, all-at-once formulation.Vortragende(r)V.Kha HuynhKontaktSenka Haznadar (senka.haznadar@aau.at)

VeranstaltungsortN.2.01Veranstalter Institut für StatistikBeschreibungThe paper considers estimation and inference in cointegrating polynomial regressions,i. e., regressions that include deterministic variables, integrated processes andtheir powers as explanatory variables. The stationary errors are allowed to be seriallycorrelated and the regressors to be endogenous. We show that estimating suchrelationships using the Phillips and Hansen (1990) fully modified OLS approach developedfor linear cointegrating relationships by incorrectly considering all integratedregressors and their powers as integrated regressors leads to the same limiting distributionas the Wagner and Hong (2016) fully modified type estimator developed forcointegrating polynomial regressions. The only restriction for this result to hold isthat all integrated variables themselves are included as regressors. Key ingredientsfor our results are novel limit results for kernel weighted sums of properly scalednonstationary processes involving powers of integrated processes and a functionalcentral limit theorem involving polynomials of Brownian motions as both integrandand integrator. Even though simulation results indicate performance advantages ofthe Wagner and Hong (2016) estimator that are partly present even in large samples,the results of the paper drastically enlarge the useability of the Phillips and Hansen(1990) estimator implemented in many software packages.Vortragende(r)Univ.-Prof. Dr. Martin WagnerInstitut für VolkswirtschaftslehreUniversität KlagenfurtKontaktSimone Gahleitner (simone.gahleitner@aau.at)