Discrepant Mass Estimates in the Cluster of Galaxies Abell 1689

We present a new mass estimate of a well-studied gravitational lensing cluster, Abell 1689, from deep Chandra observations with a total exposure of 200 ks. Within r=200 h-1 kpc, the X-ray mass estimate is systematically lower than that of lensing by 30-50%. At r>200 h-1 kpc, the mass density profiles from X-ray and weak lensing methods give consistent results. The most recent weak lensing work suggest a steeper profile than what is found from the X-ray analysis, while still in agreement with the mass at large radii. Previous studies have suggested that cooler small-scale structures can bias X-ray temperature measurements or that the northern part of the cluster is disturbed. We find these scenarios unlikely to resolve the central mass discrepancy since the former requires 70-90% of the space to be occupied by these cool structures and excluding the northern substructure does not significantly affect the total mass profiles. A more plausible explanation is a projection effect. We also find that the previously reported high hard-band to broad-band temperature ratio in A1689, and many other clusters observed with Chandra, may be resulting from the instrumental absorption that decreases 10-15% of the effective area at ~1.75 keV.Comment: 18 pages, 15 figures. ApJ accepte

The Stellar Dynamics of Omega Centauri

The stellar dynamics of Omega Centauri are inferred from the radial velocities of 469 stars measured with CORAVEL (Mayor et al. 1997). Rather than fit the data to a family of models, we generate estimates of all dynamical functions nonparametrically, by direct operation on the data. The cluster is assumed to be oblate and edge-on but mass is not assumed to follow light. The mean motions are consistent with axisymmetry but the rotation is not cylindrical. The peak rotational velocity is 7.9 km/s at 11 pc from the center. The apparent rotation of Omega Centauri is attributable in part to its proper motion. We reconstruct the stellar velocity ellipsoid as a function of position, assuming isotropy in the meridional plane. We find no significant evidence for a difference between the velocity dispersions parallel and perpendicular to the meridional plane. The mass distribution inferred from the kinematics is slightly more extended than, though not strongly inconsistent with, the luminosity distribution. We also derive the two-integral distribution function f(E,Lz) implied by the velocity data.Comment: 25 Latex pages, 12 Postscript figures, uses aastex, epsf.sty. Submitted to The Astronomical Journal, December 199

Profiling time course expression of virus genes---an illustration of Bayesian inference under shape restrictions

There have been several studies of the genome-wide temporal transcriptional program of viruses, based on microarray experiments, which are generally useful in the construction of gene regulation network. It seems that biological interpretations in these studies are directly based on the normalized data and some crude statistics, which provide rough estimates of limited features of the profile and may incur biases. This paper introduces a hierarchical Bayesian shape restricted regression method for making inference on the time course expression of virus genes. Estimates of many salient features of the expression profile like onset time, inflection point, maximum value, time to maximum value, area under curve, etc. can be obtained immediately by this method. Applying this method to a baculovirus microarray time course expression data set, we indicate that many biological questions can be formulated quantitatively and we are able to offer insights into the baculovirus biology.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS258 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The discriminative functional mixture model for a comparative analysis of bike sharing systems

Bike sharing systems (BSSs) have become a means of sustainable intermodal transport and are now proposed in many cities worldwide. Most BSSs also provide open access to their data, particularly to real-time status reports on their bike stations. The analysis of the mass of data generated by such systems is of particular interest to BSS providers to update system structures and policies. This work was motivated by interest in analyzing and comparing several European BSSs to identify common operating patterns in BSSs and to propose practical solutions to avoid potential issues. Our approach relies on the identification of common patterns between and within systems. To this end, a model-based clustering method, called FunFEM, for time series (or more generally functional data) is developed. It is based on a functional mixture model that allows the clustering of the data in a discriminative functional subspace. This model presents the advantage in this context to be parsimonious and to allow the visualization of the clustered systems. Numerical experiments confirm the good behavior of FunFEM, particularly compared to state-of-the-art methods. The application of FunFEM to BSS data from JCDecaux and the Transport for London Initiative allows us to identify 10 general patterns, including pathological ones, and to propose practical improvement strategies based on the system comparison. The visualization of the clustered data within the discriminative subspace turns out to be particularly informative regarding the system efficiency. The proposed methodology is implemented in a package for the R software, named funFEM, which is available on the CRAN. The package also provides a subset of the data analyzed in this work.Comment: Published at http://dx.doi.org/10.1214/15-AOAS861 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Subsurface characterization of groundwater contaminated by landfill leachate using microbial community profile data and a nonparametric decision-making process

Microbial biodiversity in groundwater and soil presents a unique opportunity for improving characterization and monitoring at sites with multiple contaminants, yet few computational methods use or incorporate these data because of their high dimensionality and variability. We present a systematic, nonparametric decision-making methodology to help characterize a water quality gradient in leachate-contaminated groundwater using only microbiological data for input. The data-driven methodology is based on clustering a set of molecular genetic-based microbial community profiles. Microbes were sampled from groundwater monitoring wells located within and around an aquifer contaminated with landfill leachate. We modified a self-organizing map (SOM) to weight the input variables by their relative importance and provide statistical guidance for classifying sample similarities. The methodology includes the following steps: (1) preprocessing the microbial data into a smaller number of independent variables using principal component analysis, (2) clustering the resulting principal component (PC) scores using a modified SOM capable of weighting the input PC scores by the percent variance explained by each score, and (3) using a nonparametric statistic to guide selection of appropriate groupings for management purposes. In this landfill leachate application, the weighted SOM assembles the microbial community data from monitoring wells into groupings believed to represent a gradient of site contamination that could aid in characterization and long-term monitoring decisions. Groupings based solely on microbial classifications are consistent with classifications of water quality from hydrochemical information. These microbial community profile data and improved decision-making strategy compliment traditional chemical groundwater analyses for delineating spatial zones of groundwater contamination. © 2011 by the American Geophysical Union