A CUSUM test with sliding reference for ground resonance monitoring

Ground resonance is potentially destructive oscillations that may develop on helicopters rotors when the aircraft is on or near the ground. Therefore, this unstable phenomenon has to be detected before it occurs in order to be avoided by the pilot. To predict the zones of instability, works have generally relayed on off-line modal analysis of the helicopter model. Unfortunately, this off-line analysis is not sufficiently reliable. The subspace-based cumulative sum CUSUM test, able of on-line monitoring, is a good alternative which permits - at once- to avoid the system identification for each flight point and to have more robust detection, with reduced costs. In this paper, we describe an alternative test- with a moving reference this time- in order to kill wrong alarms or premature responses that are observed for fixed-reference tests. Numerical results reported herein are driven from simulation data

Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS407 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Inhomogeneous Dependency Modelling with Time Varying Copulae

Measuring dependence in a multivariate time series is tantamount to modelling its dynamic structure in space and time. In the context of a multivariate normally distributed time series, the evolution of the covariance (or correlation) matrix over time describes this dynamic. A wide variety of applications, though, requires a modelling framework different from the multivariate normal. In risk management the non-normal behaviour of most financial time series calls for nonlinear (i.e. non-gaussian) dependency. The correct modelling of non-gaussian dependencies is therefore a key issue in the analysis of multivariate time series. In this paper we use copulae functions with adaptively estimated time varying parameters for modelling the distribution of returns, free from the usual normality assumptions. Further, we apply copulae to estimation of Value-at-Risk (VaR) of a portfolio and show its better performance over the RiskMetrics approach, a widely used methodology for VaR estimation.Value-at-Risk, time varying copula, adaptive estimation, nonparametric estimation.

The Post-Pericenter Evolution of the Galactic Center Source G2

In early 2014 the fast-moving near-infrared source G2 reached its closest approach to the supermassive black hole Sgr A* in the Galactic Center. We report on the evolution of the ionized gaseous component and the dusty component of G2 immediately after this event, revealed by new observations obtained in 2015 and 2016 with the SINFONI integral field spectrograph and the NACO imager at the ESO VLT. The spatially resolved dynamics of the Br$\gamma$ line emission can be accounted for by the ballistic motion and tidal shearing of a test-particle cloud that has followed a highly eccentric Keplerian orbit around the black hole for the last 12 years. The non-detection of a drag force or any strong hydrodynamic interaction with the hot gas in the inner accretion zone limits the ambient density to less than a few 10$^3$ cm$^{-3}$ at the distance of closest approach (1500 $R_s$), assuming G2 is a spherical cloud moving through a stationary and homogeneous atmosphere. The dust continuum emission is unresolved in L'-band, but stays consistent with the location of the Br$\gamma$ emission. The total luminosity of the Br$\gamma$ and L' emission has remained constant to within the measurement uncertainty. The nature and origin of G2 are likely related to that of the precursor source G1, since their orbital evolution is similar, though not identical. Both object are also likely related to a trailing tail structure, which is continuously connected to G2 over a large range in position and radial velocity.Comment: 17 pages, 12 figures; accepted for publication in Ap