542 research outputs found
Microstructure Effects on Daily Return Volatility in Financial Markets
We simulate a series of daily returns from intraday price movements initiated
by microstructure elements. Significant evidence is found that daily returns
and daily return volatility exhibit first order autocorrelation, but trading
volume and daily return volatility are not correlated, while intraday
volatility is. We also consider GARCH effects in daily return series and show
that estimates using daily returns are biased from the influence of the level
of prices. Using daily price changes instead, we find evidence of a significant
GARCH component. These results suggest that microstructure elements have a
considerable influence on the return generating process.Comment: 15 pages, as presented at the Complexity Workshop in Aix-en-Provenc
A Bayesian reassessment of nearest-neighbour classification
The k-nearest-neighbour procedure is a well-known deterministic method used
in supervised classification. This paper proposes a reassessment of this
approach as a statistical technique derived from a proper probabilistic model;
in particular, we modify the assessment made in a previous analysis of this
method undertaken by Holmes and Adams (2002,2003), and evaluated by Manocha and
Girolami (2007), where the underlying probabilistic model is not completely
well-defined. Once a clear probabilistic basis for the k-nearest-neighbour
procedure is established, we derive computational tools for conducting Bayesian
inference on the parameters of the corresponding model. In particular, we
assess the difficulties inherent to pseudo-likelihood and to path sampling
approximations of an intractable normalising constant, and propose a perfect
sampling strategy to implement a correct MCMC sampler associated with our
model. If perfect sampling is not available, we suggest using a Gibbs sampling
approximation. Illustrations of the performance of the corresponding Bayesian
classifier are provided for several benchmark datasets, demonstrating in
particular the limitations of the pseudo-likelihood approximation in this
set-up
An approximate Bayesian marginal likelihood approach for estimating finite mixtures
Estimation of finite mixture models when the mixing distribution support is
unknown is an important problem. This paper gives a new approach based on a
marginal likelihood for the unknown support. Motivated by a Bayesian Dirichlet
prior model, a computationally efficient stochastic approximation version of
the marginal likelihood is proposed and large-sample theory is presented. By
restricting the support to a finite grid, a simulated annealing method is
employed to maximize the marginal likelihood and estimate the support. Real and
simulated data examples show that this novel stochastic
approximation--simulated annealing procedure compares favorably to existing
methods.Comment: 16 pages, 1 figure, 3 table
Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distributions
The probability density function of the acoustic field amplitude scattered by
the seafloor was measured in a rocky environment off the coast of Norway using
a synthetic aperture sonar system, and is reported here in terms of the
probability of false alarm. Interpretation of the measurements focused on
finding appropriate class of statistical models (single versus two-component
mixture models), and on appropriate models within these two classes. It was
found that two-component mixture models performed better than single models.
The two mixture models that performed the best (and had a basis in the physics
of scattering) were a mixture between two K distributions, and a mixture
between a Rayleigh and generalized Pareto distribution. Bayes' theorem was used
to estimate the probability density function of the mixture model parameters.
It was found that the K-K mixture exhibits significant correlation between its
parameters. The mixture between the Rayleigh and generalized Pareto
distributions also had significant parameter correlation, but also contained
multiple modes. We conclude that the mixture between two K distributions is the
most applicable to this dataset.Comment: 15 pages, 7 figures, Accepted to the Journal of the Acoustical
Society of Americ
Quantitative assessment of sewer overflow performance with climate change in northwest England
Changes in rainfall patterns associated with climate change can affect the operation of a combined sewer system, with the potential increase in rainfall amount. This could lead to excessive spill frequencies and could also introduce hazardous substances into the receiving waters, which, in turn, would have an impact on the quality of shellfish and bathing waters. This paper quantifies the spilling volume, duration and frequency of 19 combined sewer overflows (CSOs) to receiving waters under two climate change scenarios, the high (A1FI), and the low emissions (B1) scenarios, simulated by three global climate models (GCMs), for a study catchment in northwest England. The future rainfall is downscaled, using climatic variables from HadCM3, CSIRO and CGCM2 GCMs, with the use of a hybrid generalized linear–artificial neural network model. The results from the model simulation for the future in 2080 showed an annual increase of 37% in total spill volume, 32% in total spill duration, and 12% in spill frequency for the shellfish water limiting requirements. These results were obtained, under the high emissions scenario, as projected by the HadCM3 as maximum. Nevertheless, the catchment drainage system is projected to cope with the future conditions in 2080 by all three GCMs. The results also indicate that under scenario B1, a significant drop was projected by CSIRO, which in the worst case could reach up to 50% in spill volume, 39% in spill duration and 25% in spill frequency. The results further show that, during the bathing season, a substantial drop is expected in the CSO spill drivers, as predicted by all GCMs under both scenarios
Characterizing and Improving Generalized Belief Propagation Algorithms on the 2D Edwards-Anderson Model
We study the performance of different message passing algorithms in the two
dimensional Edwards Anderson model. We show that the standard Belief
Propagation (BP) algorithm converges only at high temperature to a paramagnetic
solution. Then, we test a Generalized Belief Propagation (GBP) algorithm,
derived from a Cluster Variational Method (CVM) at the plaquette level. We
compare its performance with BP and with other algorithms derived under the
same approximation: Double Loop (DL) and a two-ways message passing algorithm
(HAK). The plaquette-CVM approximation improves BP in at least three ways: the
quality of the paramagnetic solution at high temperatures, a better estimate
(lower) for the critical temperature, and the fact that the GBP message passing
algorithm converges also to non paramagnetic solutions. The lack of convergence
of the standard GBP message passing algorithm at low temperatures seems to be
related to the implementation details and not to the appearance of long range
order. In fact, we prove that a gauge invariance of the constrained CVM free
energy can be exploited to derive a new message passing algorithm which
converges at even lower temperatures. In all its region of convergence this new
algorithm is faster than HAK and DL by some orders of magnitude.Comment: 19 pages, 13 figure
Microwave observations of spinning dust emission in NGC6946
We report new cm-wave measurements at five frequencies between 15 and 18GHz
of the continuum emission from the reportedly anomalous "region 4" of the
nearby galaxy NGC6946. We find that the emission in this frequency range is
significantly in excess of that measured at 8.5GHz, but has a spectrum from
15-18GHz consistent with optically thin free-free emission from a compact HII
region. In combination with previously published data we fit four emission
models containing different continuum components using the Bayesian spectrum
analysis package radiospec. These fits show that, in combination with data at
other frequencies, a model with a spinning dust component is slightly preferred
to those that possess better-established emission mechanisms.Comment: submitted MNRA
First results from the Very Small Array -- IV. Cosmological parameter estimation
We investigate the constraints on basic cosmological parameters set by the
first compact-configuration observations of the Very Small Array (VSA), and
other cosmological data sets, in the standard inflationary LambdaCDM model.
Using a weak prior 40 < H_0 < 90 km/s/Mpc and 0 < tau < 0.5 we find that the
VSA and COBE_DMR data alone produce the constraints Omega_tot =
1.03^{+0.12}_{-0.12}, Omega_bh^2 = 0.029^{+0.009}_{-0.009}, Omega_cdm h^2 =
0.13^{+0.08}_{-0.05} and n_s = 1.04^{+0.11}_{-0.08} at the 68 per cent
confidence level. Adding in the type Ia supernovae constraints, we additionally
find Omega_m = 0.32^{+0.09}_{-0.06} and Omega_Lambda = 0.71^{+0.07}_{-0.07}.
These constraints are consistent with those found by the BOOMERanG, DASI and
MAXIMA experiments. We also find that, by combining all the recent CMB
experiments and assuming the HST key project limits for H_0 (for which the
X-ray plus Sunyaev--Zel'dovich route gives a similar result), we obtain the
tight constraints Omega_m=0.28^{+0.14}_{-0.07} and Omega_Lambda=
0.72^{+0.07}_{-0.13}, which are consistent with, but independent of, those
obtained using the supernovae data.Comment: 10 pages, 6 figures, MNRAS in pres
An excess of emission in the dark cloud LDN 1111 with the Arcminute Microkelvin Imager
We present observations of the Lynds' dark nebula LDN 1111 made at microwave
frequencies between 14.6 and 17.2 GHz with the Arcminute Microkelvin Imager
(AMI). We find emission in this frequency band in excess of a thermal
free--free spectrum extrapolated from data at 1.4 GHz with matched uv-coverage.
This excess is > 15 sigma above the predicted emission. We fit the measured
spectrum using the spinning dust model of Drain & Lazarian (1998a) and find the
best fitting model parameters agree well with those derived from Scuba data for
this object by Visser et al. (2001).Comment: accepted MNRA
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