8,596 research outputs found
Lognormal Distributions and Geometric Averages of Positive Definite Matrices
This article gives a formal definition of a lognormal family of probability
distributions on the set of symmetric positive definite (PD) matrices, seen as
a matrix-variate extension of the univariate lognormal family of distributions.
Two forms of this distribution are obtained as the large sample limiting
distribution via the central limit theorem of two types of geometric averages
of i.i.d. PD matrices: the log-Euclidean average and the canonical geometric
average. These averages correspond to two different geometries imposed on the
set of PD matrices. The limiting distributions of these averages are used to
provide large-sample confidence regions for the corresponding population means.
The methods are illustrated on a voxelwise analysis of diffusion tensor imaging
data, permitting a comparison between the various average types from the point
of view of their sampling variability.Comment: 28 pages, 8 figure
Jet energy calibration at the LHC
Jets are one of the most prominent physics signatures of high energy proton
proton (p-p) collisions at the Large Hadron Collider (LHC). They are key
physics objects for precision measurements and searches for new phenomena. This
review provides an overview of the reconstruction and calibration of jets at
the LHC during its first Run. ATLAS and CMS developed different approaches for
the reconstruction of jets, but use similar methods for the energy calibration.
ATLAS reconstructs jets utilizing input signals from their calorimeters and use
charged particle tracks to refine their energy measurement and suppress the
effects of multiple p-p interactions (pileup). CMS, instead, combines
calorimeter and tracking information to build jets from particle flow objects.
Jets are calibrated using Monte Carlo (MC) simulations and a residual in situ
calibration derived from collision data is applied to correct for the
differences in jet response between data and Monte Carlo. Large samples of
dijet, Z+jets, and photon+jet events at the LHC allowed the calibration of jets
with high precision, leading to very small systematic uncertainties. Both ATLAS
and CMS achieved a jet energy calibration uncertainty of about 1% in the
central detector region and for jets with transverse momentum pT>100 GeV. At
low jet pT, the jet energy calibration uncertainty is less than 4%, with
dominant contributions from pileup, differences in energy scale between quark
and gluon jets, and jet flavor composition.Comment: Article submitted to the International Journal of Modern Physics A
(IJMPA) as part of the special issue on the "Jet Measurements at the LHC",
editor G. Dissertor
Multiple Testing of Local Maxima for Detection of Peaks in Random Fields
A topological multiple testing scheme is presented for detecting peaks in
images under stationary ergodic Gaussian noise, where tests are performed at
local maxima of the smoothed observed signals. The procedure generalizes the
one-dimensional scheme of Schwartzman et al. (2011) to Euclidean domains of
arbitrary dimension. Two methods are developed according to two different ways
of computing p-values: (i) using the exact distribution of the height of local
maxima (Cheng and Schwartzman, 2014), available explicitly when the noise field
is isotropic; (ii) using an approximation to the overshoot distribution of
local maxima above a pre-threshold (Cheng and Schwartzman, 2014), applicable
when the exact distribution is unknown, such as when the stationary noise field
is non-isotropic. The algorithms, combined with the Benjamini-Hochberg
procedure for thresholding p-values, provide asymptotic strong control of the
False Discovery Rate (FDR) and power consistency, with specific rates, as the
search space and signal strength get large. The optimal smoothing bandwidth and
optimal pre-threshold are obtained to achieve maximum power. Simulations show
that FDR levels are maintained in non-asymptotic conditions. The methods are
illustrated in a nanoscopy image analysis problem of detecting fluorescent
molecules against the image background.Comment: 30 pages, 5 figures. arXiv admin note: text overlap with
arXiv:1203.306
Standardization of multivariate Gaussian mixture models and background adjustment of PET images in brain oncology
In brain oncology, it is routine to evaluate the progress or remission of the
disease based on the differences between a pre-treatment and a post-treatment
Positron Emission Tomography (PET) scan. Background adjustment is necessary to
reduce confounding by tissue-dependent changes not related to the disease. When
modeling the voxel intensities for the two scans as a bivariate Gaussian
mixture, background adjustment translates into standardizing the mixture at
each voxel, while tumor lesions present themselves as outliers to be detected.
In this paper, we address the question of how to standardize the mixture to a
standard multivariate normal distribution, so that the outliers (i.e., tumor
lesions) can be detected using a statistical test. We show theoretically and
numerically that the tail distribution of the standardized scores is favorably
close to standard normal in a wide range of scenarios while being conservative
at the tails, validating voxelwise hypothesis testing based on standardized
scores. To address standardization in spatially heterogeneous image data, we
propose a spatial and robust multivariate expectation-maximization (EM)
algorithm, where prior class membership probabilities are provided by
transformation of spatial probability template maps and the estimation of the
class mean and covariances are robust to outliers. Simulations in both
univariate and bivariate cases suggest that standardized scores with soft
assignment have tail probabilities that are either very close to or more
conservative than standard normal. The proposed methods are applied to a real
data set from a PET phantom experiment, yet they are generic and can be used in
other contexts
INFLATION TARGET ZONES AS A COMMITMENT MECHANISM
In a simple new keyenesian model of monetary policy under discretion constraining the Central Bank to put inflation within a pre-specified Inflation Target Zone can eliminate the inflation bias and, at least for certain parameter ranges, significantly reduce the stabilization bias. Also, it is possible to investigate what is the optimal Inflation Target Zone for different economies. These seem to depend of the structural parameters in a non-linear and often non-monotonic way.
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