524 research outputs found
Exploiting Domain Knowledge in Making Delegation Decisions
@inproceedings{conf/admi/EmeleNSP11, added-at = {2011-12-19T00:00:00.000+0100}, author = {Emele, Chukwuemeka David and Norman, Timothy J. and Sensoy, Murat and Parsons, Simon}, biburl = {http://www.bibsonomy.org/bibtex/20a08b683088443f1fd36d6ef28bf6615/dblp}, booktitle = {ADMI}, crossref = {conf/admi/2011}, editor = {Cao, Longbing and Bazzan, Ana L. C. and Symeonidis, Andreas L. and Gorodetsky, Vladimir and Weiss, Gerhard and Yu, Philip S.}, ee = {http://dx.doi.org/10.1007/978-3-642-27609-5_9}, interhash = {1d7e7f8554e8bdb3d43c32e02aeabcec}, intrahash = {0a08b683088443f1fd36d6ef28bf6615}, isbn = {978-3-642-27608-8}, keywords = {dblp}, pages = {117-131}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2011-12-19T00:00:00.000+0100}, title = {Exploiting Domain Knowledge in Making Delegation Decisions.}, url = {http://dblp.uni-trier.de/db/conf/admi/admi2011.html#EmeleNSP11}, volume = 7103, year = 2011 }Postprin
Covariance forecasting in equity markets
We compare the performance of popular covariance forecasting models in the context of a portfolio of major European equity indices. We find that models based on high-frequency data offer a clear advantage in terms of statistical accuracy. They also yield more theoretically consistent predictions from an empirical asset pricing perspective, and, lead to superior out-of-sample portfolio performance. Overall, a parsimonious Vector Heterogeneous Autoregressive (VHAR) model that involves lagged daily, weekly and monthly realised covariances achieves the best performance out of the competing models. A promising new simple hybrid covariance estimator is developed that exploits option-implied information and high-frequency data while adjusting for the volatility riskpremium. Relative model performance does not change during the global financial crisis, or, if a different forecast horizon, or, intraday sampling frequency is employed. Finally, our evidence remains robust when we consider an alternative sample of U.S. stocks
AEGIS: Infrared Spectral Energy Distributions of MIPS 70micron selected sources
We present 0.5 -160 micron Spectral Energy Distributions (SEDs) of galaxies,
detected at 70microns with the Multiband Imaging Photometer for Spitzer (MIPS),
using broadband imaging data from Spitzer and ground-based telescopes.
Spectroscopic redshifts, in the range 0.2<z<1.5, have been measured as part of
the Deep Extragalactic Evolutionary Probe2 (DEEP2) project. Based on the SEDs
we explore the nature and physical properties of the sources. Using the optical
spectra we derive Hbeta and [OII]-based Star Formation Rates (SFR) which are
10-100 times lower than SFR estimates based on IR and radio. The median offset
in SFR between optical and IR is reduced by a factor of ~3 when we apply a
typical extinction corrections. We investigate mid-to-far infrared correlations
for low redshift (>0.5) and high redshift (0.5<z<1.2) bins. Using this unique
``far-infrared'' selected sample we derive an empirical mid to far-infrared
relationship that can be used to estimate the infrared energy budget of
galaxies in the high-redshift universe. Our sample can be used as a template to
translate far-infrared luminosities into bolometric luminosities for high
redshift objects.Comment: 4 pages, 5 figures, accepted for publication in AEGIS ApJL Special
Issu
Algorithms for Visualizing Phylogenetic Networks
We study the problem of visualizing phylogenetic networks, which are
extensions of the Tree of Life in biology. We use a space filling visualization
method, called DAGmaps, in order to obtain clear visualizations using limited
space. In this paper, we restrict our attention to galled trees and galled
networks and present linear time algorithms for visualizing them as DAGmaps.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
Panchromatic spectral energy distributions of Herschel sources
(abridged) Far-infrared Herschel photometry from the PEP and HerMES programs
is combined with ancillary datasets in the GOODS-N, GOODS-S, and COSMOS fields.
Based on this rich dataset, we reproduce the restframe UV to FIR ten-colors
distribution of galaxies using a superposition of multi-variate Gaussian modes.
The median SED of each mode is then fitted with a modified version of the
MAGPHYS code that combines stellar light, emission from dust heated by stars
and a possible warm dust contribution heated by an AGN. The defined Gaussian
grouping is also used to identify rare sources. The zoology of outliers
includes Herschel-detected ellipticals, very blue z~1 Ly-break galaxies,
quiescent spirals, and torus-dominated AGN with star formation. Out of these
groups and outliers, a new template library is assembled, consisting of 32 SEDs
describing the intrinsic scatter in the restframe UV-to-submm colors of
infrared galaxies. This library is tested against L(IR) estimates with and
without Herschel data included, and compared to eight other popular methods
often adopted in the literature. When implementing Herschel photometry, these
approaches produce L(IR) values consistent with each other within a median
absolute deviation of 10-20%, the scatter being dominated more by fine tuning
of the codes, rather than by the choice of SED templates. Finally, the library
is used to classify 24 micron detected sources in PEP GOODS fields. AGN appear
to be distributed in the stellar mass (M*) vs. star formation rate (SFR) space
along with all other galaxies, regardless of the amount of infrared luminosity
they are powering, with the tendency to lie on the high SFR side of the "main
sequence". The incidence of warmer star-forming sources grows for objects with
higher specific star formation rates (sSFR), and they tend to populate the
"off-sequence" region of the M*-SFR-z space.Comment: Accepted for publication in A&A. Some figures are presented in low
resolution. The new galaxy templates are available for download at the
address http://www.mpe.mpg.de/ir/Research/PEP/uvfir_temp
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