Interpolating fields of carbon monoxide data using a hybrid statistical-physical model

Atmospheric Carbon Monoxide (CO) provides a window on the chemistry of the atmosphere since it is one of few chemical constituents that can be remotely sensed, and it can be used to determine budgets of other greenhouse gases such as ozone and OH radicals. Remote sensing platforms in geostationary Earth orbit will soon provide regional observations of CO at several vertical layers with high spatial and temporal resolution. However, cloudy locations cannot be observed and estimates of the complete CO concentration fields have to be estimated based on the cloud-free observations. The current state-of-the-art solution of this interpolation problem is to combine cloud-free observations with prior information, computed by a deterministic physical model, which might introduce uncertainties that do not derive from data. While sharing features with the physical model, this paper suggests a Bayesian hierarchical model to estimate the complete CO concentration fields. The paper also provides a direct comparison to state-of-the-art methods. To our knowledge, such a model and comparison have not been considered before.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS168 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Sensitivity of nucleon-nucleus scattering to the off-shell behavior of on-shell equivalent NN potentials

The sensitivity of nucleon-nucleus elastic scattering to the off-shell behavior of realistic nucleon-nucleon interactions is investigated when on-shell equivalent nucleon-nucleon potentials are used. The study is based on applications of the full-folding optical model potential for an explicit treatment of the off-shell behavior of the nucleon-nucleon effective interaction. Applications were made at beam energies between 40 and 500 MeV for proton scattering from 40Ca and 208Pb. We use the momentum-dependent Paris potential and its local on-shell equivalent as obtained with the Gelfand-Levitan and Marchenko inversion formalism for the two nucleon Schroedinger equation. Full-folding calculations for nucleon-nucleus scattering show small fluctuations in the corresponding observables. This implies that off-shell features of the NN interaction cannot be unambiguously identified with these processes. Inversion potentials were also constructed directly from NN phase-shift data (SM94) in the 0-1.3 GeV energy range. Their use in proton-nucleus scattering above 200 MeV provide a superior description of the observables relative to those obtained from current realistic NN potentials. Limitations and scope of our findings are presented and discussed.Comment: 17 pages tightened REVTeX, 8 .ps figures, submitted to Phys. Rev.

Energy Dependence of the NN t-matrix in the Optical Potential for Elastic Nucleon-Nucleus Scattering

The influence of the energy dependence of the free NN t-matrix on the optical potential of nucleon-nucleus elastic scattering is investigated within the context of a full-folding model based on the impulse approximation. The treatment of the pole structure of the NN t-matrix, which has to be taken into account when integrating to negative energies is described in detail. We calculate proton-nucleus elastic scattering observables for $^{16}$O, $^{40}$Ca, and $^{208}$Pb between 65 and 200 MeV laboratory energy and study the effect of the energy dependence of the NN t-matrix. We compare this result with experiment and with calculations where the center-of-mass energy of the NN t-matrix is fixed at half the projectile energy. It is found that around 200 MeV the fixed energy approximation is a very good representation of the full calculation, however deviations occur when going to lower energies (65 MeV).Comment: 11 pages (revtex), 6 postscript figure

Bayesian inference in spherical linear models: robustness and conjugate analysis

AbstractThe early work of Zellner on the multivariate Student-t linear model has been extended to Bayesian inference for linear models with dependent non-normal error terms, particularly through various papers by Osiewalski, Steel and coworkers. This article provides a full Bayesian analysis for a spherical linear model. The density generator of the spherical distribution is here allowed to depend both on the precision parameter φ and on the regression coefficients β. Another distinctive aspect of this paper is that proper priors for the precision parameter are discussed.The normal-chi-squared family of prior distributions is extended to a new class, which allows the posterior analysis to be carried out analytically. On the other hand, a direct joint modelling of the data vector and of the parameters leads to conjugate distributions for the regression and the precision parameters, both individually and jointly. It is shown that some model specifications lead to Bayes estimators that do not depend on the choice of the density generator, in agreement with previous results obtained in the literature under different assumptions. Finally, the distribution theory developed to tackle the main problem is useful on its own right

Underidentification?

We study the identification of an econometric model that is linear in parameters from a generalized-method-of-moments perspective. We regard underidentification as a set of over- identifying restrictions imposed on an augmented structural model. Therefore, our proposal is to test for underidentification by testing for overidentification in the augmented model using standard methods that are available in the literature. As examples we consider intertemporal asset pricing and dynamic panel data models.