16,799 research outputs found
Robust Inference of Risks of Large Portfolios
We propose a bootstrap-based robust high-confidence level upper bound (Robust
H-CLUB) for assessing the risks of large portfolios. The proposed approach
exploits rank-based and quantile-based estimators, and can be viewed as a
robust extension of the H-CLUB method (Fan et al., 2015). Such an extension
allows us to handle possibly misspecified models and heavy-tailed data. Under
mixing conditions, we analyze the proposed approach and demonstrate its
advantage over the H-CLUB. We further provide thorough numerical results to
back up the developed theory. We also apply the proposed method to analyze a
stock market dataset.Comment: 45 pages, 2 figure
New Techniques for High-Contrast Imaging with ADI: the ACORNS-ADI SEEDS Data Reduction Pipeline
We describe Algorithms for Calibration, Optimized Registration, and Nulling
the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized
software package to reduce high-contrast imaging data, and its application to
data from the SEEDS survey. We implement several new algorithms, including a
method to register saturated images, a trimmed mean for combining an image
sequence that reduces noise by up to ~20%, and a robust and computationally
fast method to compute the sensitivity of a high-contrast observation
everywhere on the field-of-view without introducing artificial sources. We also
include a description of image processing steps to remove electronic artifacts
specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed
analysis of the Locally Optimized Combination of Images (LOCI) algorithm
commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in
python. It is efficient and open-source, and includes several optional features
which may improve performance on data from other instruments. ACORNS-ADI
requires minimal modification to reduce data from instruments other than
HiCIAO. It is freely available for download at
www.github.com/t-brandt/acorns-adi under a BSD license.Comment: 15 pages, 9 figures, accepted to ApJ. Replaced with accepted version;
mostly minor changes. Software update
A Spatial Cliff-Ord-type Model with Heteroskedastic Innovations: Small and Large Sample Results
In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step GMM/IV type estimation procedure for the parameters of the model. We then establish the limiting distribution of our suggested estimators, and give consistent estimators for their asymptotic variance covariance matrices, utilizing results given in Kelejian and Prucha (2007b). Monte Carlo results are given which suggest that the derived large sample distribution provides a good approximation to the actual small sample distribution of our estimators.
Exact algorithms for -TV regularization of real-valued or circle-valued signals
We consider -TV regularization of univariate signals with values on the
real line or on the unit circle. While the real data space leads to a convex
optimization problem, the problem is non-convex for circle-valued data. In this
paper, we derive exact algorithms for both data spaces. A key ingredient is the
reduction of the infinite search spaces to a finite set of configurations,
which can be scanned by the Viterbi algorithm. To reduce the computational
complexity of the involved tabulations, we extend the technique of distance
transforms to non-uniform grids and to the circular data space. In total, the
proposed algorithms have complexity where is the length
of the signal and is the number of different values in the data set. In
particular, the complexity is for quantized data. It is the
first exact algorithm for TV regularization with circle-valued data, and it is
competitive with the state-of-the-art methods for scalar data, assuming that
the latter are quantized
Statistical properties of the dark matter haloes of dwarf galaxies and correlations with the environment
According to the now strongly supported concordance CDM model,
galaxies may be grossly described as a luminous component embedded in a dark
matter halo. The density profile of these mass dominating haloes may be
determined by N - body simulations which mimic the evolution of the tiny
initial density perturbations during the process leading to the structures we
observe today. Unfortunately, when the effect of baryons is taken into account,
the situation gets much more complicated due to the difficulties in simulating
their physics. As a consequence, a definitive prediction of how dark matter
haloes should presently look like is still missing. We revisit here this issue
from an observational point of view devoting our attention to dwarf galaxies.
Being likely dark matter dominated, these systems are ideal candidates to
investigate the present day halo density profiles and check whether dark matter
related quantities correlate with the stellar ones or the environment. By
fitting a large sample of well measured rotation curves, we infer constraints
on both halo structural parameters (such as the logarithmic slope of the
density profile and its concentration) and derived quantities (e.g., the mass
fraction and the Newtonian acceleration) which could then be used to constrain
galaxy formation scenarios. Moreover, we investigate whether the halo
properties correlates with the environment the galaxy lives in thus offering a
new tool to deepen our understanding of galaxy formation.Comment: 14 pages, 8 tables, 5 figures, accepted for publication on MNRA
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