A comment on the article “A collinearity diagnosis of the GNSS geocenter determination” by P. Rebischung, Z. Altamimi, and T. Springer

Meindl et al. (Adv Space Res 51(7):1047–1064, 2013) showed that the geocenter z -component estimated from observations of global navigation satellite systems (GNSS) is strongly correlated to a particular parameter of the solar radiation pressure (SRP) model developed by Beutler et al. (Manuscr Geod 19:367–386, 1994). They analyzed the forces caused by SRP and the impact on the satellites’ orbits. The authors achieved their results using perturbation theory and celestial mechanics. Rebischung et al. (J Geod doi:10.1016/j.asr.2012.10.026, 2013) also deal with the geocenter determination with GNSS. The authors carried out a collinearity diagnosis of the associated parameter estimation problem. They conclude “without much exaggerating that current GNSS are insensitive to any component of geocenter motion”. They explain this inability by the high degree of collinearity of the geocenter coordinates mainly with satellite clock corrections. Based on these results and additional experiments, they state that the conclusions drawn by Meindl et al. (Adv Space Res 51(7):1047–1064, 2013) are questionable. We do not agree with these conclusions and present our arguments in this article. In the first part, we review and highlight the main characteristics of the studies performed by Meindl et al. (Adv Space Res 51(7):1047–1064, 2013) to show that the experiments are quite different from those performed by Rebischung et al. (J Geod doi:10.1016/j.asr.2012.10.026,2013) . In the second part, we show that normal equation (NEQ) systems are regular when estimating geocenter coordinates, implying that the covariance matrices associated with the NEQ systems may be used to assess the sensitivity to geocenter coordinates in a standard way. The sensitivity of GNSS to the components of the geocenter is discussed. Finally, we comment on the arguments raised by Rebischung et al. (J Geod doi:10.1016/j.asr.2012.10.026, 2013) against the results of Meindl et al. (Adv Space Res 51(7):1047–1064, 2013).

Δ I = 1 / 2 rule for kaon decays derived from QCD infrared fixed point

This article gives details of our proposal to replace ordinary chiral SU(3)L×SU(3)R perturbation theory χPT3 by three-flavor chiral-scale perturbation theory χPTσ. In χPTσ, amplitudes are expanded at low energies and small u,d,s quark masses about an infrared fixed point αIR of three-flavor QCD. At αIR, the quark condensate ⟨q¯q⟩vac≠0 induces nine Nambu-Goldstone bosons: π,K,η, and a 0++ QCD dilaton σ. Physically, σ appears as the f0(500) resonance, a pole at a complex mass with real part ≲ mK. The ΔI=1/2 rule for nonleptonic K decays is then a consequence of χPTσ, with a KSσ coupling fixed by data for γγ→ππ and KS→γγ. We estimate RIR≈5 for the nonperturbative Drell-Yan ratio R=σ(e+e−→hadrons)/σ(e+e−→μ+μ−) at αIR and show that, in the many-color limit, σ/f0 becomes a narrow qq¯ state with planar-gluon corrections. Rules for the order of terms in χPTσ loop expansions are derived in Appendix A and extended in Appendix B to include inverse-power Li-Pagels singularities due to external operators. This relates to an observation that, for γγ channels, partial conservation of the dilatation current is not equivalent to σ-pole dominance.

Coherence and Elicitability

The risk of a financial position is usually summarized by a risk measure. As this risk measure has to be estimated from historical data, it is important to be able to verify and compare competing estimation procedures. In statistical decision theory, risk measures for which such verification and comparison is possible, are called elicitable. It is known that quantile-based risk measures such as value at risk are elicitable. In this paper, the existing result of the nonelicitability of expected shortfall is extended to all law-invariant spectral risk measures unless they reduce to minus the expected value. Hence, it is unclear how to perform forecast verification or comparison. However, the class of elicitable law-invariant coherent risk measures does not reduce to minus the expected value. We show that it consists of certain expectiles.

All-loop anomalous dimensions in integrable λ-deformed σ-models

We calculate the all-loop anomalous dimensions of current operators in λ-deformed σ-models. For the isotropic integrable deformation and for a semi-simple group G we compute the anomalous dimensions using two different methods. In the first we use the all-loop effective action and in the second we employ perturbation theory along with the Callan–Symanzik equation and in conjunction with a duality-type symmetry shared by these models. Furthermore, using CFT techniques we compute the all-loop anomalous dimension of bilinear currents for the isotropic deformation case and a general G . Finally we work out the anomalous dimension matrix for the cases of anisotropic SU(2) and the two couplings, corresponding to the symmetric coset G/H and a subgroup H, splitting of a group G.

Statistical inference for the optimal approximating model

In the setting of high-dimensional linear models with Gaussian noise, we investigate the possibility of confidence statements connected to model selection. Although there exist numerous procedures for adaptive (point) estimation, the construction of adaptive confidence regions is severely limited (cf. Li in Ann Stat 17:1001–1008, 1989). The present paper sheds new light on this gap. We develop exact and adaptive confidence regions for the best approximating model in terms of risk. One of our constructions is based on a multiscale procedure and a particular coupling argument. Utilizing exponential inequalities for noncentral χ2-distributions, we show that the risk and quadratic loss of all models within our confidence region are uniformly bounded by the minimal risk times a factor close to one.

Applications of random set theory in econometrics

In recent years, the econometrics literature has shown a growing interest in the study of partially identified models, in which the object of economic and statistical interest is a set rather than a point. The characterization of this set and the development of consistent estimators and inference procedures for it with desirable properties are the main goals of partial identification analysis. This review introduces the fundamental tools of the theory of random sets, which brings together elements of topology, convex geometry, and probability theory to develop a coherent mathematical framework to analyze random elements whose realizations are sets. It then elucidates how these tools have been fruitfully applied in econometrics to reach the goals of partial identification analysis.