826 research outputs found
Detecting a stochastic background of gravitational waves in the presence of non-Gaussian noise: A performance of generalized cross-correlation statistic
We discuss a robust data analysis method to detect a stochastic background of
gravitational waves in the presence of non-Gaussian noise. In contrast to the
standard cross-correlation (SCC) statistic frequently used in the stochastic
background searches, we consider a {\it generalized cross-correlation} (GCC)
statistic, which is nearly optimal even in the presence of non-Gaussian noise.
The detection efficiency of the GCC statistic is investigated analytically,
particularly focusing on the statistical relation between the false-alarm and
the false-dismissal probabilities, and the minimum detectable amplitude of
gravitational-wave signals. We derive simple analytic formulae for these
statistical quantities. The robustness of the GCC statistic is clarified based
on these formulae, and one finds that the detection efficiency of the GCC
statistic roughly corresponds to the one of the SCC statistic neglecting the
contribution of non-Gaussian tails. This remarkable property is checked by
performing the Monte Carlo simulations and successful agreement between
analytic and simulation results was found.Comment: 15 pages, 8 figures, presentation and some figures modified, final
version to be published in PR
Density-functional study of Cu atoms, monolayers, and coadsorbates on polar ZnO surfaces
The structure and electronic properties of single Cu atoms, copper monolayers
and thin copper films on the polar oxygen and zinc terminated surfaces of ZnO
are studied using periodic density-functional calculations. We find that the
binding energy of Cu atoms sensitively depends on how charge neutrality of the
polar surfaces is achieved. Bonding is very strong if the surfaces are
stabilized by an electronic mechanism which leads to partially filled surface
bands. As soon as the surface bands are filled (either by partial Cu coverage,
by coadsorbates, or by the formation of defects), the binding energy decreases
significantly. In this case, values very similar to those found for nonpolar
surfaces and for copper on finite ZnO clusters are obtained. Possible
implications of these observations concerning the growth mode of copper on
polar ZnO surfaces and their importance in catalysis are discussed.Comment: 6 pages with 2 postscript figures embedded. Uses REVTEX and epsf
macro
Parameter Estimation in Astronomy with Poisson-Distributed Data. I. The Chi-Square-Gamma Statistic
Applying the standard weighted mean formula, [sum_i {n_i sigma^{-2}_i}] /
[sum_i {sigma^{-2}_i}], to determine the weighted mean of data, n_i, drawn from
a Poisson distribution, will, on average, underestimate the true mean by ~1 for
all true mean values larger than ~3 when the common assumption is made that the
error of the ith observation is sigma_i = max(sqrt{n_i},1). This small, but
statistically significant offset, explains the long-known observation that
chi-square minimization techniques which use the modified Neyman's chi-square
statistic, chi^2_{N} equiv sum_i (n_i-y_i)^2 / max(n_i,1), to compare
Poisson-distributed data with model values, y_i, will typically predict a total
number of counts that underestimates the true total by about 1 count per bin.
Based on my finding that the weighted mean of data drawn from a Poisson
distribution can be determined using the formula [sum_i [n_i + min(n_i,1)]
(n_i+1)^{-1}] / [sum_i (n_i+1)^{-1}], I propose that a new chi-square
statistic, chi^2_gamma equiv sum_i [n_i + min(n_i,1) - y_i]^2 / [n_i + 1],
should always be used to analyze Poisson-distributed data in preference to the
modified Neyman's chi-square statistic. I demonstrate the power and usefulness
of chi-square-gamma minimization by using two statistical fitting techniques
and five chi-square statistics to analyze simulated X-ray power-law 15-channel
spectra with large and small counts per bin. I show that chi-square-gamma
minimization with the Levenberg-Marquardt or Powell's method can produce
excellent results (mean slope errors <=3%) with spectra having as few as 25
total counts.Comment: 22 pages (LaTeX+aaspp4.sty), 6 tables (PostScript format) and 12
figures (PostScript format). The PostScript version of the paper, tables, and
full-resolution color figures are available at
http://www.noao.edu/staff/mighell/chi-square-gamma/ To appear in the
Astrophysical Journal (accepted 1998 November 20
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General specification testing with locally misspecified models
A well known result is that many of the tests used in econometrics such as the Rao score (RS) test, may not be robust to misspecified alternatives, that is, when the alternative model does not correspond to the underlying data generating process. Under this scenario, these tests spuriously reject the null hypothesis too often. We generalize this result to GMM based tests. We also extend the method proposed in Bera and Yoon (Econometric Theory 9, 1993) for constructing RS tests that are robust to local misspecification to GMM based tests. Finally, a further generalization for general estimating and testing functions is developed. This framework encompasses both the Bera-Yoon likelihood based results as well as its use in the GMM environment
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A New Robust and Most Powerful Test in the Presence of Local Misspeci cation
This paper proposes a new test that is consistent, achieves correct asymptotic size and is locally most powerful under local misspecification, and when any square-root-of-n-estimator of the nuisance parameters is used. The new test can be seen as an extension of the Bera and Yoon (1993) procedure that deals with non-ML estimation, while preserving its optimality properties. Similarly, the proposed test extends Neyman's (1959) C(a) test to handle locally misspecified alternatives. A Monte Carlo study investigates the finite sample performance in terms of size, power and robustness to misspecification
First-principles calculation on the transport properties of molecular wires between Au clusters under equilibrium
Based on the matrix Green's function method combined with hybrid
tight-binding / density functional theory, we calculate the conductances of a
series of gold-dithiol molecule-gold junctions including benzenedithiol (BDT),
benzenedimethanethiol (BDMT), hexanedithiol (HDT), octanedithiol (ODT) and
decanedithiol (DDT). An atomically-contacted extended molecule model is used in
our calculation. As an important procedure, we determine the position of the
Fermi level by the energy reference according to the results from ultraviolet
photoelectron spectroscopy (UPS) experiments. After considering the
experimental uncertainty in UPS measurement, the calculated results of
molecular conductances near the Fermi level qualitatively agree with the
experimental values measured by Tao et. al. [{\it Science} 301, 1221 (2003);
{\it J. Am. Chem. Soc.} 125, 16164 (2003); {\it Nano. Lett.} 4, 267 (2004).]Comment: 12 pages,8 figure
Inference with interference between units in an fMRI experiment of motor inhibition
An experimental unit is an opportunity to randomly apply or withhold a
treatment. There is interference between units if the application of the
treatment to one unit may also affect other units. In cognitive neuroscience, a
common form of experiment presents a sequence of stimuli or requests for
cognitive activity at random to each experimental subject and measures
biological aspects of brain activity that follow these requests. Each subject
is then many experimental units, and interference between units within an
experimental subject is likely, in part because the stimuli follow one another
quickly and in part because human subjects learn or become experienced or
primed or bored as the experiment proceeds. We use a recent fMRI experiment
concerned with the inhibition of motor activity to illustrate and further
develop recently proposed methodology for inference in the presence of
interference. A simulation evaluates the power of competing procedures.Comment: Published by Journal of the American Statistical Association at
http://www.tandfonline.com/doi/full/10.1080/01621459.2012.655954 . R package
cin (Causal Inference for Neuroscience) implementing the proposed method is
freely available on CRAN at https://CRAN.R-project.org/package=ci
V-Proportion: a method based on the Voronoi diagram to study spatial relations in neuronal mosaics of the retina
The visual system plays a predominant role in the human perception. Although all components of the eye are important to perceive visual information, the retina is a fundamental part of the visual system. In this work we study the spatial relations between neuronal mosaics in the retina. These relations have shown its importance to investigate possible constraints or connectivities between different spatially colocalized populations of neurons, and to explain how visual information spreads along the layers before being sent to the brain. We introduce the V-Proportion, a method based on the Voronoi diagram to study possible spatial interactions between two neuronal mosaics. Results in simulations as well as in real data demonstrate the effectiveness of this method to detect spatial relations between neurons in different layers
Structure and Magnetism of Neutral and Anionic Palladium Clusters
The properties of neutral and anionic Pd_N clusters were investigated with
spin-density-functional calculations. The ground state structures are
three-dimensional for N>3 and they are magnetic with a spin-triplet for 2<=N<=7
and a spin nonet for N=13 neutral clusters. Structural- and spin-isomers were
determined and an anomalous increase of the magnetic moment with temperature is
predicted for a Pd_7 ensemble. Vertical electron detachment and ionization
energies were calculated and the former agree well with measured values for
anionic Pd_N clusters.Comment: 5 pages, 3 figures, fig. 2 in color, accepted to Phys. Rev. Lett.
(2001
A global descriptor of spatial pattern interaction in the galaxy distribution
We present the function J as a morphological descriptor for point patterns
formed by the distribution of galaxies in the Universe. This function was
recently introduced in the field of spatial statistics, and is based on the
nearest neighbor distribution and the void probability function. The J
descriptor allows to distinguish clustered (i.e. correlated) from ``regular''
(i.e. anti-correlated) point distributions. We outline the theoretical
foundations of the method, perform tests with a Matern cluster process as an
idealised model of galaxy clustering, and apply the descriptor to galaxies and
loose groups in the Perseus-Pisces Survey. A comparison with mock-samples
extracted from a mixed dark matter simulation shows that the J descriptor can
be profitably used to constrain (in this case reject) viable models of cosmic
structure formation.Comment: Significantly enhanced version, 14 pages, LaTeX using epsf, aaspp4, 7
eps-figures, accepted for publication in the Astrophysical Journa
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