2,616 research outputs found
Fourier methods for smooth distribution function estimation
In this paper we show how to use Fourier transform methods to analyze the
asymptotic behavior of kernel distribution function estimators. Exact
expressions for the mean integrated squared error in terms of the
characteristic function of the distribution and the Fourier transform of the
kernel are employed to obtain the limit value of the optimal bandwidth sequence
in its greatest generality. The assumptions in our results are mild enough so
that they are applicable when the kernel used in the estimator is a
superkernel, or even the sinc kernel, and this allows to extract some
interesting consequences, as the existence of a class of distributions for
which the kernel estimator achieves a first-order improvement in efficiency
over the empirical distribution function.Comment: 12 pages, 2 figure
Dark Matter Halo Structure in CDM Hydrodynamical Simulations
We have carried out a comparative analysis of the properties of dark matter
halos in N-body and hydrodynamical simulations. We analyze their density
profiles, shapes and kinematical properties with the aim of assessing the
effects that hydrodynamical processes might produce on the evolution of the
dark matter component. The simulations performed allow us to reproduce dark
matter halos with high resolution, although the range of circular velocities is
limited. We find that for halos with circular velocities of at the virial radius, the presence of baryons affects the evolution of
the dark matter component in the central region modifying the density profiles,
shapes and velocity dispersions. We also analyze the rotation velocity curves
of disk-like structures and compare them with observational results.Comment: 28 pages, 15 figures (figures 3ab sent by request), 2 tables.
Accepted for publication MNRA
Combinando testes de Mardia e BHEP na avaliação duma hipótese multivariada de normalidade
https://thekeep.eiu.edu/den_1998_feb/1015/thumbnail.jp
Boundary kernels for distribution function estimation
Boundary effects for kernel estimators of curves with compact supports are well known in regression and density estimation frameworks. In this paper we address the use of boundary kernels for distribution function estimation. We establish the Chung-Smirnov law of iterated logarithm and an asymptotic expansion for the mean integrated squared error of the proposed estimator. These results show the superior theoretical performance of the boundary modified kernel estimator over the classical kernel estimator for distribution functions that are not smooth at the extreme points of the distribution support. The automatic selection of the bandwidth is also briefly discussed in this paper. Beta reference distribution and cross-validation bandwidth selectors are considered. Simulations suggest that the cross-validation bandwidth performs well, although the simpler reference distribution bandwidth is quite effective for the generality of test distributions
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