30,478 research outputs found
A Factor-Adjusted Multiple Testing Procedure with Application to Mutual Fund Selection
In this article, we propose a factor-adjusted multiple testing (FAT)
procedure based on factor-adjusted p-values in a linear factor model involving
some observable and unobservable factors, for the purpose of selecting skilled
funds in empirical finance. The factor-adjusted p-values were obtained after
extracting the latent common factors by the principal component method. Under
some mild conditions, the false discovery proportion can be consistently
estimated even if the idiosyncratic errors are allowed to be weakly correlated
across units. Furthermore, by appropriately setting a sequence of threshold
values approaching zero, the proposed FAT procedure enjoys model selection
consistency. Extensive simulation studies and a real data analysis for
selecting skilled funds in the U.S. financial market are presented to
illustrate the practical utility of the proposed method. Supplementary
materials for this article are available online
Effects of center offset and noise on weak-lensing derived concentration-mass relation of dark matter halos
With the halo catalog from the {\it Millennium Simulation}, we analyze the
weak-lensing measured density profiles for clusters of galaxies, paying
attention to the determination of the concentration-mass (-) relation
which can be biased by the center offset, selection effect, and shape noise
from intrinsic ellipticities of background galaxies. Several different methods
of locating the center of a cluster from weak-lensing effects alone are
explored. We find that, for intermediate redshift clusters, the highest peak
from our newly proposed two-scale smoothing method applied to the reconstructed
convergence field, first with a smoothing scale of and then
, corresponds best to the true center. Assuming the
parameterized Navarro-Frenk-White profile, we fit the reduced tangential shear
signals around different centers identified by different methods. It is shown
that, for the ensemble median values, a center offset larger than one scale
radius can bias the derived mass and concentration significantly lower
than the true values, especially for low-mass halos. However, the existence of
noise can compensate for the offset effect and reduce the systematic bias,
although the scatter of mass and concentration becomes considerably larger.
Statistically, the bias effect of center offset on the - relation is
insignificant if an appropriate center finding method is adopted. On the other
hand, noise from intrinsic ellipticities can bias the - relation derived
from a sample of weak-lensing analyzed clusters if a simple fitting
method is used. To properly account for the scatter and covariance between
and , we apply a Bayesian method to improve the statistical analysis of the
- relation. It is shown that this new method allows us to derive the
- relation with significantly reduced biases.Comment: Accepted for Publication in ApJ. 25 pages, 14 figures. Updated to
match the published versio
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