65,888 research outputs found
Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages
The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the
theory of multivariate time series; however, in practice, identifiability
issues have led many authors to abandon VARMA modeling in favor of the simpler
Vector AutoRegressive (VAR) model. Such a practice is unfortunate since even
very simple VARMA models can have quite complicated VAR representations. We
narrow this gap with a new optimization-based approach to VARMA identification
that is built upon the principle of parsimony. Among all equivalent
data-generating models, we seek the parameterization that is "simplest" in a
certain sense. A user-specified strongly convex penalty is used to measure
model simplicity, and that same penalty is then used to define an estimator
that can be efficiently computed. We show that our estimator converges to a
parsimonious element in the set of all equivalent data-generating models, in a
double asymptotic regime where the number of component time series is allowed
to grow with sample size. Further, we derive non-asymptotic upper bounds on the
estimation error of our method relative to our specially identified target.
Novel theoretical machinery includes non-asymptotic analysis of infinite-order
VAR, elastic net estimation under a singular covariance structure of
regressors, and new concentration inequalities for quadratic forms of random
variables from Gaussian time series. We illustrate the competitive performance
of our methods in simulation and several application domains, including
macro-economic forecasting, demand forecasting, and volatility forecasting
Testing Alternative Theories of Gravity using LISA
We investigate the possible bounds which could be placed on alternative
theories of gravity using gravitational wave detection from inspiralling
compact binaries with the proposed LISA space interferometer. Specifically, we
estimate lower bounds on the coupling parameter \omega of scalar-tensor
theories of the Brans-Dicke type and on the Compton wavelength of the graviton
\lambda_g in hypothetical massive graviton theories. In these theories,
modifications of the gravitational radiation damping formulae or of the
propagation of the waves translate into a change in the phase evolution of the
observed gravitational waveform. We obtain the bounds through the technique of
matched filtering, employing the LISA Sensitivity Curve Generator (SCG),
available online. For a neutron star inspiralling into a 10^3 M_sun black hole
in the Virgo Cluster, in a two-year integration, we find a lower bound \omega >
3 * 10^5. For lower-mass black holes, the bound could be as large as 2 * 10^6.
The bound is independent of LISA arm length, but is inversely proportional to
the LISA position noise error. Lower bounds on the graviton Compton wavelength
ranging from 10^15 km to 5 * 10^16 km can be obtained from one-year
observations of massive binary black hole inspirals at cosmological distances
(3 Gpc), for masses ranging from 10^4 to 10^7 M_sun. For the highest-mass
systems (10^7 M_sun), the bound is proportional to (LISA arm length)^{1/2} and
to (LISA acceleration noise)^{-1/2}. For the others, the bound is independent
of these parameters because of the dominance of white-dwarf confusion noise in
the relevant part of the frequency spectrum. These bounds improve and extend
earlier work which used analytic formulae for the noise curves.Comment: 16 pages, 9 figures, submitted to Classical & Quantum Gravit
Empirical Bayes selection of wavelet thresholds
This paper explores a class of empirical Bayes methods for level-dependent
threshold selection in wavelet shrinkage. The prior considered for each wavelet
coefficient is a mixture of an atom of probability at zero and a heavy-tailed
density. The mixing weight, or sparsity parameter, for each level of the
transform is chosen by marginal maximum likelihood. If estimation is carried
out using the posterior median, this is a random thresholding procedure; the
estimation can also be carried out using other thresholding rules with the same
threshold. Details of the calculations needed for implementing the procedure
are included. In practice, the estimates are quick to compute and there is
software available. Simulations on the standard model functions show excellent
performance, and applications to data drawn from various fields of application
are used to explore the practical performance of the approach. By using a
general result on the risk of the corresponding marginal maximum likelihood
approach for a single sequence, overall bounds on the risk of the method are
found subject to membership of the unknown function in one of a wide range of
Besov classes, covering also the case of f of bounded variation. The rates
obtained are optimal for any value of the parameter p in (0,\infty],
simultaneously for a wide range of loss functions, each dominating the L_q norm
of the \sigmath derivative, with \sigma\ge0 and 0<q\le2.Comment: Published at http://dx.doi.org/10.1214/009053605000000345 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Mean reversion in stock index futures markets: a nonlinear analysis
Several stylized theoretical models of futures basis behavior under nonzero transactions costs predict nonlinear mean reversion of the futures basis towards its equilibrium value. Nonlinearly mean-reverting models are employed to characterize the basis of the SandP 500 and the FTSE 100 indices over the post-1987 crash period, capturing empirically these theoretical predictions and examining the view that the degree of mean reversion in the basis is a function of the size of the deviation from equilibrium. The estimated half lives of basis shocks, obtained using Monte Carlo integration methods, suggest that for smaller shocks to the basis level the basis displays substantial persistence, while for larger shocks the basis exhibits highly nonlinear mean reversion towards its equilibrium value. © 2002 Wiley Periodicals, Inc
On the Coverage Bound Problem of Empirical Likelihood Methods For Time Series
The upper bounds on the coverage probabilities of the confidence regions
based on blockwise empirical likelihood [Kitamura (1997)] and nonstandard
expansive empirical likelihood [Nordman et al. (2013)] methods for time series
data are investigated via studying the probability for the violation of the
convex hull constraint. The large sample bounds are derived on the basis of the
pivotal limit of the blockwise empirical log-likelihood ratio obtained under
the fixed-b asymptotics, which has been recently shown to provide a more
accurate approximation to the finite sample distribution than the conventional
chi-square approximation. Our theoretical and numerical findings suggest that
both the finite sample and large sample upper bounds for coverage probabilities
are strictly less than one and the blockwise empirical likelihood confidence
region can exhibit serious undercoverage when (i) the dimension of moment
conditions is moderate or large; (ii) the time series dependence is positively
strong; or (iii) the block size is large relative to sample size. A similar
finite sample coverage problem occurs for the nonstandard expansive empirical
likelihood. To alleviate the coverage bound problem, we propose to penalize
both empirical likelihood methods by relaxing the convex hull constraint.
Numerical simulations and data illustration demonstrate the effectiveness of
our proposed remedies in terms of delivering confidence sets with more accurate
coverage
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
Absolute pose estimation is a fundamental problem in computer vision, and it
is a typical parameter estimation problem, meaning that efforts to solve it
will always suffer from outlier-contaminated data. Conventionally, for a fixed
dimensionality d and the number of measurements N, a robust estimation problem
cannot be solved faster than O(N^d). Furthermore, it is almost impossible to
remove d from the exponent of the runtime of a globally optimal algorithm.
However, absolute pose estimation is a geometric parameter estimation problem,
and thus has special constraints. In this paper, we consider pairwise
constraints and propose a globally optimal algorithm for solving the absolute
pose estimation problem. The proposed algorithm has a linear complexity in the
number of correspondences at a given outlier ratio. Concretely, we first
decouple the rotation and the translation subproblems by utilizing the pairwise
constraints, and then we solve the rotation subproblem using the
branch-and-bound algorithm. Lastly, we estimate the translation based on the
known rotation by using another branch-and-bound algorithm. The advantages of
our method are demonstrated via thorough testing on both synthetic and
real-world dataComment: 10 pages, 7figure
Graph Sample and Hold: A Framework for Big-Graph Analytics
Sampling is a standard approach in big-graph analytics; the goal is to
efficiently estimate the graph properties by consulting a sample of the whole
population. A perfect sample is assumed to mirror every property of the whole
population. Unfortunately, such a perfect sample is hard to collect in complex
populations such as graphs (e.g. web graphs, social networks etc), where an
underlying network connects the units of the population. Therefore, a good
sample will be representative in the sense that graph properties of interest
can be estimated with a known degree of accuracy. While previous work focused
particularly on sampling schemes used to estimate certain graph properties
(e.g. triangle count), much less is known for the case when we need to estimate
various graph properties with the same sampling scheme. In this paper, we
propose a generic stream sampling framework for big-graph analytics, called
Graph Sample and Hold (gSH). To begin, the proposed framework samples from
massive graphs sequentially in a single pass, one edge at a time, while
maintaining a small state. We then show how to produce unbiased estimators for
various graph properties from the sample. Given that the graph analysis
algorithms will run on a sample instead of the whole population, the runtime
complexity of these algorithm is kept under control. Moreover, given that the
estimators of graph properties are unbiased, the approximation error is kept
under control. Finally, we show the performance of the proposed framework (gSH)
on various types of graphs, such as social graphs, among others
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