A multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic studies in the presence of non-evaluable subjects

Diagnostic test accuracy studies observe the result of a gold standard procedure that defines the presence or absence of a disease and the result of a diagnostic test. They typically report the number of true positives, false positives, true negatives and false negatives. However, diagnostic test outcomes can also be either non-evaluable positives or non-evaluable negatives. We propose a novel model for the meta-analysis of diagnostic studies in the presence of non-evaluable outcomes, which assumes independent multinomial distributions for the true and non-evaluable positives, and, the true and non-evaluable negatives, conditional on the latent sensitivity, specificity, probability of non-evaluable positives and probability of non-evaluable negatives in each study. For the random effects distribution of the latent proportions, we employ a drawable vine copula that can successively model the dependence in the joint tails. Our methodology is demonstrated with an extensive simulation study and applied to data from diagnostic accuracy studies of coronary computed tomography angiography for the detection of coronary artery disease. The comparison of our method with the existing approaches yields findings in the real data application that change the current conclusions

Asymptotics of joint maxima for discontinuous random variables

This paper explores the joint extreme-value behavior of discontinuous random variables. It is shown that as in the continuous case, the latter is characterized by the weak limit of the normalized componentwise maxima and the convergence of any compatible copula. Illustrations are provided and an extension to the case of triangular arrays is considered which sheds new light on recent work of Coles and Pauli (Stat Probab Lett 54:373-379, 2001) and Mitov and Nadarajah (Extremes 8:357-370, 2005). This leads to considerations on the meaning of the bivariate upper tail dependence coefficient of Joe (Comput Stat Data Anal 16:279-297, 1993) in the discontinuous cas

Test Station for Magnetization Measurements on Large Quantities of Superconducting Strands

In the superconducting main magnets of the Large Hadron Collider (LHC), persistent currents in the superconductor determine the field quality at injection field. For this reason it is necessary to check the magnetization of the cable strands during their production. During four years, this requires measurements of the width of the strand magnetization hysteresis loop at 0.5 T, 1.9 K, at a rate of up to eight samples per day. This paper describes the design, construction and the first results of a magnetization test station built for this purpose. The samples are cooled in a cryostat, with a 2-m long elliptic tail. This tail is inserted in a normal conducting dipole magnet with a field between ± 1.5 T. Racetrack pick-up coils, integrated in the cryostat, detect the voltage due to flux change, which is then integrated numerically. The sample holder can contain eight strand samples, each 20 cm long. The test station operates in two modes: either the sample is fixed while the external field is changed, or the sample is moved while the field remains constant. First results of calibration measurements with nickel and niobium are reported

Modelling stochastic bivariate mortality

Stochastic mortality, i.e. modelling death arrival via a jump process with stochastic intensity, is gaining increasing reputation as a way to represent mortality risk. This paper represents a first attempt to model the mortality risk of couples of individuals, according to the stochastic intensity approach. On the theoretical side, we extend to couples the Cox processes set up, i.e. the idea that mortality is driven by a jump process whose intensity is itself a stochastic process, proper of a particular generation within each gender. Dependence between the survival times of the members of a couple is captured by an Archimedean copula. On the calibration side, we fit the joint survival function by calibrating separately the (analytical) copula and the (analytical) margins. First, we select the best fit copula according to the methodology of Wang and Wells (2000) for censored data. Then, we provide a sample-based calibration for the intensity, using a time-homogeneous, non mean-reverting, affine process: this gives the analytical marginal survival functions. Coupling the best fit copula with the calibrated margins we obtain, on a sample generation, a joint survival function which incorporates the stochastic nature of mortality improvements and is far from representing independency.On the contrary, since the best fit copula turns out to be a Nelsen one, dependency is increasing with age and long-term dependence exists

1.9 K Test Facility for the Reception of the Superconducting Cables for the LHC

A new test facility (called FRESCA) is under construction at CERN to measure the electrical properties of the LHC superconducting cables. Its main features compared to existing test facilities are: a) independently cooled background magnet, b) test currents up to 32 kA, c) temperature between 1.8 and 4.5 K, d) long measurement length of 60 cm, e) field perpendicular or parallel to the cable face, f) measurement of the current distribution between the strands. The facility consists of an outer cryostat containing a superconducting NbTi dipole magnet with a bore of 56 mm and a maximum operating field of 9.5 T. The current through the magnet is supplied by an external 16 kA power supply and fed into the cryostat using self-cooled leads. The lower bath of the cryostat, separated by means of a so called lambda-plate from the upper bath, can be cooled down to 1.9 K using a subcooled superfluid refrigeration system. Within the outer cryostat, an inner cryostat is installed, containing the superconducting cable samples. This approach makes it possible to change samples while keeping the background magnet cold, and thus decreasing the helium consumption and cool-down time of the samples. The cable samples are connected through self-cooled leads to an external 32 kA power supply. The lower bath of the inner cryostat, containing the sample holder, is separated by means of a so called lambda-plate from the upper bath and can be cooled down to 1.9 K. The samples can be rotated while remaining at liquid helium temperature, enabling measurements with the background field perpendicular or parallel to the broad face of the cable. Several arrays of Hall probes are installed next to the samples in order to estimate possible current imbalances between the strands of the cables

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC