Optimality of Quasi-Score in the multivariate mean-variance model with an application to the zero-inflated Poisson model with measurement errors

In a multivariate mean-variance model, the class of linear score (LS) estimators based on an unbiased linear estimating function is introduced. A special member of this class is the (extended) quasi-score (QS) estimator. It is ``extended'' in the sense that it comprises the parameters describing the distribution of the regressor variables. It is shown that QS is (asymptotically) most efficient within the class of LS estimators. An application is the multivariate measurement error model, where the parameters describing the regressor distribution are nuisance parameters. A special case is the zero-inflated Poisson model with measurement errors, which can be treated within this framework

Towards the optimal window for the 2MASS dipole

A comparison of the 2MASS flux dipole to the CMB dipole can serve as a method to constrain a combination of the cosmological parameter Omega_m and the luminosity bias of the 2MASS survey. For this constraint to be as tight as possible, it is necessary to maximize the correlation between the two dipoles. This can be achieved by optimizing the survey window through which the flux dipole is measured. Here we explicitly construct such a window for the 2MASS survey. The optimization in essence reduces to excluding from the calculation of the flux dipole galaxies brighter than some limiting magnitude K_min of the near-infrared K_s band. This exclusion mitigates nonlinear effects and shot noise from small scales, which decorrelate the 2MASS dipole from the CMB dipole. Under the assumption of negligible shot noise we find that the optimal value of K_min is about five. Inclusion of shot noise shifts the optimal K_min to larger values. We present an analytical formula for shot noise for the 2MASS flux dipole, to be used in follow-up work with 2MASS data. The misalignment angle between the two dipoles is a sensitive measure of their correlation: the higher the correlation, the smaller the expectation value of the angle. A minimum of the misalignment is thus a sign of the optimal gravity window. We model analytically the distribution function for the misalignment angle and show that the misalignment estimated by Maller et al. is consistent with the assumed underlying model (though it is greater than the expectation value). We predict with about 90% confidence that the misalignment will decrease if 2MASS galaxies brighter than K_min = 5 mag are excluded from the calculation of the flux dipole. This prediction has been indirectly confirmed by the results of Erdogdu et al. (ABRIDGED)Comment: 14 pages, 3 figures. Significantly expanded version, with added sections on shot noise and likelihood for beta, as well as an appendix with a derivation of the distribution for the misalignment angle relaxing the small-angle assumptio

Towards an explanation of orbits in the extreme trans-Neptunian region: The effect of Milgromian dynamics

Milgromian dynamics (MD or MOND) uniquely predicts motion in a galaxy from the distribution of its stars and gas in a remarkable agreement with observations so far. In the solar system, MD predicts the existence of some possibly non-negligible dynamical effects, which can be used to constrain the freedom in MD theories. Known extreme trans-Neptunian objects (ETNOs) have their argument of perihelion, longitude of ascending node, and inclination distributed in highly non-uniform fashion; ETNOs are bodies with perihelion distances greater than the orbit of Neptune and with semimajor axes greater than 150 au and less than $\sim1500$ au. It is as if these bodies have been systematically perturbed by some external force. We investigated a hypothesis that the puzzling orbital characteristics of ETNOs are a consequence of MD. We set up a dynamical model of the solar system incorporating the external field effect (EFE), which is anticipated to be the dominant effect of MD in the ETNOs region. We used constraints available on the strength of EFE coming from radio tracking of the Cassini spacecraft. We performed several numerical experiments, concentrating on the long-term orbital evolution of primordial (randomised) ETNOs in MD. The EFE could produce distinct non-uniform distributions of the orbital elements of ETNOs that are related to the orientation of an orbit in space. If we demand that EFE is solely responsible for the detachment of Sedna and 2012 VP$_{113}$, then these distributions are at odds with the currently observed statistics on ETNOs unless the EFE quadrupole strength parameter $Q_{2}$ has values that are unlikely (with probability < 1$\%$) in light of the Cassini data.Comment: 19 pages, 19 figures, 4 tables; accepted for publication in A&A; v2 - language improve

Volatility and covariation of financial assets: a high-frequency analysis

Using high frequency data for the price dynamics of equities we measure the impact that market microstructure noise has on estimates of the: (i) volatility of returns; and (ii) variance-covariance matrix of n. assets. We propose a Kalman-filter-based methodology that allows us to deconstruct price series into the true effcient price and the microstructure noise. This approach allows us to employ volatility estimators that achieve very low Root Mean Squared Errors (RMSEs) compared to other estimators that have been proposed to deal with market microstructure noise at high frequencies. Furthermore, this price series decomposition allows us to estimate the variance covariance matrix of n assets in a more efficient way than the methods so far proposed in the literature. We illustrate our results by calculating how microstructre noise affects portfolio decisions and calculations of the equity beta in a CAPM setting

"Are genetically modified foods bad for my health?". Individuals' valutation and the choice among different information sources

We investigate the role of information on consumers’ valuation for food products containing genetically modified organisms (GMOs), using data from a specifically designed survey. We provide three main results. First, we show that introducing mandatory labels to identify whether or not a food product contains GMOs, significantly reduces consumers’ valuation. Second, adding to the label additional information on GMOs significantly affects valuation. Third, no matter the sign of the information previously received, consumers are more willing to trust General Practitioners (GPs), the information source they prefer most. Overall, these results indicate that the crucial issue is not the presence of the label per se, but the availability of the necessary information to make good use of the label content to assess potential health risks deriving from GM foods. In particular, our findings suggest that this can be achieved by properly informing (and convincing) GPs and other health professionals that risks for human health are minimal.Genetically modified foods, information, health risks, General practitioners, labelling.

Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data