Gravity at the horizon: on relativistic effects, CMB-LSS correlations and ultra-large scales in Horndeski's theory

We address the impact of consistent modifications of gravity on the largest observable scales, focusing on relativistic effects in galaxy number counts and the cross-correlation between the matter large scale structure (LSS) distribution and the cosmic microwave background (CMB). Our analysis applies to a very broad class of general scalar-tensor theories encoded in the Horndeski Lagrangian and is fully consistent on linear scales, retaining the full dynamics of the scalar field and not assuming quasi-static evolution. As particular examples we consider self-accelerating Covariant Galileons, Brans-Dicke theory and parameterizations based on the effective field theory of dark energy, using the \hiclass\, code to address the impact of these models on relativistic corrections to LSS observables. We find that especially effects which involve integrals along the line of sight (lensing convergence, time delay and the integrated Sachs-Wolfe effect -- ISW) can be considerably modified, and even lead to $\mathcal{O}(1000\%)$ deviations from General Relativity in the case of the ISW effect for Galileon models, for which standard probes such as the growth function only vary by $\mathcal{O}(10\%)$. These effects become dominant when correlating galaxy number counts at different redshifts and can lead to $\sim 50\%$ deviations in the total signal that might be observable by future LSS surveys. Because of their integrated nature, these deep-redshift cross-correlations are sensitive to modifications of gravity even when probing eras much before dark energy domination. We further isolate the ISW effect using the cross-correlation between LSS and CMB temperature anisotropies and use current data to further constrain Horndeski models (abridged).Comment: 30 pages plus appendices, 9 figures. References added. Accepted for publication in JCA

Latent Markov model for longitudinal binary data: An application to the performance evaluation of nursing homes

Performance evaluation of nursing homes is usually accomplished by the repeated administration of questionnaires aimed at measuring the health status of the patients during their period of residence in the nursing home. We illustrate how a latent Markov model with covariates may effectively be used for the analysis of data collected in this way. This model relies on a not directly observable Markov process, whose states represent different levels of the health status. For the maximum likelihood estimation of the model we apply an EM algorithm implemented by means of certain recursions taken from the literature on hidden Markov chains. Of particular interest is the estimation of the effect of each nursing home on the probability of transition between the latent states. We show how the estimates of these effects may be used to construct a set of scores which allows us to rank these facilities in terms of their efficacy in taking care of the health conditions of their patients. The method is used within an application based on data concerning a set of nursing homes located in the Region of Umbria, Italy, which were followed for the period 2003--2005.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS230 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Item selection by Latent Class-based methods

The evaluation of nursing homes is usually based on the administration of questionnaires made of a large number of polytomous items. In such a context, the Latent Class (LC) model represents a useful tool for clustering subjects in homogenous groups corresponding to different degrees of impairment of the health conditions. It is known that the performance of model-based clustering and the accuracy of the choice of the number of latent classes may be affected by the presence of irrelevant or noise variables. In this paper, we show the application of an item selection algorithm to real data collected within a project, named ULISSE, on the quality-of-life of elderly patients hosted in italian nursing homes. This algorithm, which is closely related to that proposed by Dean and Raftery in 2010, is aimed at finding the subset of items which provides the best clustering according to the Bayesian Information Criterion. At the same time, it allows us to select the optimal number of latent classes. Given the complexity of the ULISSE study, we perform a validation of the results by means of a sensitivity analysis to different specifications of the initial subset of items and of a resampling procedure

Not invasive analyses on a tin-bronze dagger from Jericho. A case study

Tin-bronze makes its appearance in Southern Levant during the Early Bronze IV, the post-urban phase of the last centuries of the 3rdmillennium BC, when arsenical copper was still the most widespread copper alloy. Only from the following Middle Bronze Age tin-bronze will be the utmost spread alloy. The adoption of tin as alloying metal purports new technological skills, and a changed trade supply system, through new routes, thanks to itinerant coppersmiths. The examination of dagger TS.14.143 found in an EB IV (2300-2000 BC) tomb at Jericho by mean of trace elements and Energy Dispersive X-ray Diffraction analyses, provided info about its metal composition and technology. The detection of tin, testified only by a few specimens at the site so far, allows some reflections about the beginning of diffusion tin-bronze, and the presence of a small-scale melting activity in the post-urban phase in the key-site of Jericho