sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R

The estimation of kernel-smoothed relative risk functions is a useful approach to examining the spatial variation of disease risk. Though there exist several options for performing kernel density estimation in statistical software packages, there have been very few contributions to date that have focused on estimation of a relative risk function per se . Use of a variable or adaptive smoothing parameter for estimation of the individual densities has been shown to provide additional benefits in estimating relative risk and specific computational tools for this approach are essentially absent. Furthermore, little attention has been given to providing methods in available software for any kind of subsequent analysis with respect to an estimated risk function. To facilitate analyses in the field, the R package sparr is introduced, providing the ability to construct both fixed and adaptive kernel-smoothed densities and risk functions, identify statistically significant fluctuations in an estimated risk function through the use of asymptotic tolerance contours, and visualize these objects in flexible and attractive ways.

Boundary kernels for adaptive density estimators on regions with irregular boundaries

AbstractIn some applications of kernel density estimation the data may have a highly non-uniform distribution and be confined to a compact region. Standard fixed bandwidth density estimates can struggle to cope with the spatially variable smoothing requirements, and will be subject to excessive bias at the boundary of the region. While adaptive kernel estimators can address the first of these issues, the study of boundary kernel methods has been restricted to the fixed bandwidth context. We propose a new linear boundary kernel which reduces the asymptotic order of the bias of an adaptive density estimator at the boundary, and is simple to implement even on an irregular boundary. The properties of this adaptive boundary kernel are examined theoretically. In particular, we demonstrate that the asymptotic performance of the density estimator is maintained when the adaptive bandwidth is defined in terms of a pilot estimate rather than the true underlying density. We examine the performance for finite sample sizes numerically through analysis of simulated and real data sets

sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R

The estimation of kernel-smoothed relative risk functions is a useful approach to examining the spatial variation of disease risk. Though there exist several options for performing kernel density estimation in statistical software packages, there have been very few contributions to date that have focused on estimation of a relative risk function per se. Use of a variable or adaptive smoothing parameter for estimation of the individual densities has been shown to provide additional benefits in estimating relative risk and specific computational tools for this approach are essentially absent. Furthermore, little attention has been given to providing methods in available software for any kind of subsequent analysis with respect to an estimated risk function. To facilitate analyses in the field, the R package sparr is introduced, providing the ability to construct both fixed and adaptive kernel-smoothed densities and risk functions, identify statistically significant fluctuations in an estimated risk function through the use of asymptotic tolerance contours, and visualize these objects in flexible and attractive ways

Interpreting the extended emission around three nearby debris disc host stars

Cool debris discs are a relic of the planetesimal formation process around their host star, analogous to the solar system's Edgeworth-Kuiper belt. As such, they can be used as a proxy to probe the origin and formation of planetary systems like our own. The Herschel Open Time Key Programmes "DUst around NEarby Stars" (DUNES) and "Disc Emission via a Bias-free Reconnaissance in the Infrared/Submillimetre" (DEBRIS) observed many nearby, sun-like stars at far-infrared wavelengths seeking to detect and characterize the emission from their circumstellar dust. Excess emission attributable to the presence of dust was identified from around $\sim$ 20% of stars. Herschel's high angular resolution ($\sim$ 7" FWHM at 100 $\mu$m) provided the capacity for resolving debris belts around nearby stars with radial extents comparable to the solar system (50 to 100 au). As part of the DUNES and DEBRIS surveys, we obtained observations of three debris disc stars, HIP 22263 (HD 30495), HIP 62207 (HD 110897), and HIP 72848 (HD 131511), at far-infrared wavelengths with the Herschel PACS instrument. Combining these new images and photometry with ancilliary data from the literature, we undertook simultaneous multi-wavelength modelling of the discs' radial profiles and spectral energy distributions using three different methodologies: single annulus, modified black body, and a radiative transfer code. We present the first far-infrared spatially resolved images of these discs and new single-component debris disc models. We characterize the capacity of the models to reproduce the disc parameters based on marginally resolved emission through analysis of two sets of simulated systems (based on the HIP 22263 and HIP 62207 data) with the noise levels typical of the Herschel images. We find that the input parameter values are recovered well at noise levels attained in the observations presented here.Comment: 13 pages, 5 figures, 5 tables, accepted for publication in A&

The gold standard: accurate stellar and planetary parameters for eight Kepler M dwarf systems enabled by parallaxes

We report parallaxes and proper motions from the Hawaii Infrared Parallax Program for eight nearby M dwarf stars with transiting exoplanets discovered by Kepler. We combine our directly measured distances with mass-luminosity and radius–luminosity relationships to significantly improve constraints on the host stars’ properties. Our astrometry enables the identification of wide stellar companions to the planet hosts. Within our limited sample, all the multi-transiting planet hosts (three of three) appear to be single stars, while nearly all (four of five) of the systems with a single detected planet have wide stellar companions. By applying strict priors on average stellar density from our updated radius and mass in our transit fitting analysis, we measure the eccentricity probability distributions for each transiting planet. Planets in single-star systems tend to have smaller eccentricities than those in binaries, although this difference is not significant in our small sample. In the case of Kepler-42bcd, where the eccentricities are known to be ≃0, we demonstrate that such systems can serve as powerful tests of M dwarf evolutionary models by working in L⋆ − ρ⋆ space. The transit-fit density for Kepler- 42bcd is inconsistent with model predictions at 2.1σ (22%), but matches more empirical estimates at 0.2σ (2%), consistent with earlier results showing model radii of M dwarfs are underinflated. Gaia will provide high-precision parallaxes for the entire Kepler M dwarf sample, and TESS will identify more planets transiting nearby, late-type stars, enabling significant improvements in our understanding of the eccentricity distribution of small planets and the parameters of late-type dwarfs.Support for Program number HST-HF2-51364.001-A was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555.Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for MAST for non-HST data is provided by the NASA Office of Space Science via grant NNX09AF08G and by other grants and contracts. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission directorate. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper. URL: http://www.tacc.utexas.edu. (HST-HF2-51364.001-A - NASA through Space Telescope Science Institute; NAS5-26555 - NASA; NNX09AF08G - NASA Office of Space Science; NASA Science Mission directorate

Managing at the Speed of Light: Improving Mission-Support Performance

The House and Senate Energy and Water Development Appropriations Subcommittees requested this study to help DOE's three major mission-support organizations improve their operations to better meet the current and future needs of the department. The passage of the Recovery Act only increased the importance of having DOE's mission-support offices working in the most effective, efficient, and timely manner as possible. While following rules and regulations is essential, the foremost task of the mission-support offices is to support the department's mission, i.e., the programs that DOE is implementing, whether in Washington D.C. or in the field. As a result, the Panel offered specific recommendations to strengthen the mission-focus and improve the management of each of the following support functions based on five "management mandates":- Strategic Vision- Leadership- Mission and Customer Service Orientation- Tactical Implementation- Agility/AdaptabilityKey FindingsThe Panel made several recommendations in each of the functional areas examined and some overarching recommendations for the corporate management of the mission-support offices that they believed would result in significant improvements to DOE's mission-support operations. The Panel believed that adopting these recommendations will not only make DOE a better functioning organization, but that most of them are essential if DOE is to put its very large allocation of Recovery Act funding to its intended uses as quickly as possible

The trumping relation and the structure of the bipartite entangled states

The majorization relation has been shown to be useful in classifying which transformations of jointly held quantum states are possible using local operations and classical communication. In some cases, a direct transformation between two states is not possible, but it becomes possible in the presence of another state (known as a catalyst); this situation is described mathematically by the trumping relation, an extension of majorization. The structure of the trumping relation is not nearly as well understood as that of majorization. We give an introduction to this subject and derive some new results. Most notably, we show that the dimension of the required catalyst is in general unbounded; there is no integer $k$ such that it suffices to consider catalysts of dimension $k$ or less in determining which states can be catalyzed into a given state. We also show that almost all bipartite entangled states are potentially useful as catalysts.Comment: 7 pages, RevTe