20,963 research outputs found
Hawkes process as a model of social interactions: a view on video dynamics
We study by computer simulation the "Hawkes process" that was proposed in a
recent paper by Crane and Sornette (Proc. Nat. Acad. Sci. USA 105, 15649
(2008)) as a plausible model for the dynamics of YouTube video viewing numbers.
We test the claims made there that robust identification is possible for
classes of dynamic response following activity bursts. Our simulated timeseries
for the Hawkes process indeed fall into the different categories predicted by
Crane and Sornette. However the Hawkes process gives a much narrower spread of
decay exponents than the YouTube data, suggesting limits to the universality of
the Hawkes-based analysis.Comment: Added errors to parameter estimates and further description. IOP
style, 13 pages, 5 figure
Heavy-Tailed Features and Empirical Analysis of the Limit Order Book Volume Profiles in Futures Markets
This paper poses a few fundamental questions regarding the attributes of the
volume profile of a Limit Order Books stochastic structure by taking into
consideration aspects of intraday and interday statistical features, the impact
of different exchange features and the impact of market participants in
different asset sectors. This paper aims to address the following questions:
1. Is there statistical evidence that heavy-tailed sub-exponential volume
profiles occur at different levels of the Limit Order Book on the bid and ask
and if so does this happen on intra or interday time scales ?
2.In futures exchanges, are heavy tail features exchange (CBOT, CME, EUREX,
SGX and COMEX) or asset class (government bonds, equities and precious metals)
dependent and do they happen on ultra-high (<1sec) or mid-range (1sec -10min)
high frequency data?
3.Does the presence of stochastic heavy-tailed volume profile features evolve
in a manner that would inform or be indicative of market participant behaviors,
such as high frequency algorithmic trading, quote stuffing and price discovery
intra-daily?
4. Is there statistical evidence for a need to consider dynamic behavior of
the parameters of models for Limit Order Book volume profiles on an intra-daily
time scale ?
Progress on aspects of each question is obtained via statistically rigorous
results to verify the empirical findings for an unprecedentedly large set of
futures market LOB data. The data comprises several exchanges, several futures
asset classes and all trading days of 2010, using market depth (Type II) order
book data to 5 levels on the bid and ask
Absolute identification by relative judgment
In unidimensional absolute identification tasks, participants identify stimuli that vary along a single dimension. Performance is surprisingly poor compared with discrimination of the same stimuli. Existing models assume that identification is achieved using long-term representations of absolute magnitudes. The authors propose an alternative relative judgment model (RJM) in which the elemental perceptual units are representations of the differences between current and previous stimuli. These differences are used, together with the previous feedback, to respond. Without using long-term representations of absolute magnitudes, the RJM accounts for (a) information transmission limits, (b) bowed serial position effects, and (c) sequential effects, where responses are biased toward immediately preceding stimuli but away from more distant stimuli (assimilation and contrast)
COBE experience with filter QUEST
A gyro based filter variation on the standard QUEST attitude determination algorithm is applied to the Cosmic Background Explorer (COBE). Filter QUEST is found to be three times as fast as the batch estimator and slightly more accurate than regular QUEST. Perhaps more important than its speed or accuracy is the fact that Filter QUEST can provide real time attitude solutions when regular QUEST cannot, due to lack of observability. Filter QUEST is also easy to use and adjust for the proper memory length. Suitable applications for Filter QUEST include coarse and real time attitude determination
Structural Variability from Noisy Tomographic Projections
In cryo-electron microscopy, the 3D electric potentials of an ensemble of
molecules are projected along arbitrary viewing directions to yield noisy 2D
images. The volume maps representing these potentials typically exhibit a great
deal of structural variability, which is described by their 3D covariance
matrix. Typically, this covariance matrix is approximately low-rank and can be
used to cluster the volumes or estimate the intrinsic geometry of the
conformation space. We formulate the estimation of this covariance matrix as a
linear inverse problem, yielding a consistent least-squares estimator. For
images of size -by- pixels, we propose an algorithm for calculating this
covariance estimator with computational complexity
, where the condition number
is empirically in the range --. Its efficiency relies on the
observation that the normal equations are equivalent to a deconvolution problem
in 6D. This is then solved by the conjugate gradient method with an appropriate
circulant preconditioner. The result is the first computationally efficient
algorithm for consistent estimation of 3D covariance from noisy projections. It
also compares favorably in runtime with respect to previously proposed
non-consistent estimators. Motivated by the recent success of eigenvalue
shrinkage procedures for high-dimensional covariance matrices, we introduce a
shrinkage procedure that improves accuracy at lower signal-to-noise ratios. We
evaluate our methods on simulated datasets and achieve classification results
comparable to state-of-the-art methods in shorter running time. We also present
results on clustering volumes in an experimental dataset, illustrating the
power of the proposed algorithm for practical determination of structural
variability.Comment: 52 pages, 11 figure
Demonstration of a quantile system for compression of data from deep space probes
Quantile system of data compression for space telemetr
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