16,470 research outputs found
On the detection of superdiffusive behaviour in time series
We present a new method for detecting superdiffusive behaviour and for
determining rates of superdiffusion in time series data. Our method applies
equally to stochastic and deterministic time series data (with no prior
knowledge required of the nature of the data) and relies on one realisation (ie
one sample path) of the process. Linear drift effects are automatically removed
without any preprocessing. We show numerical results for time series
constructed from i.i.d. -stable random variables and from deterministic
weakly chaotic maps. We compare our method with the standard method of
estimating the growth rate of the mean-square displacement as well as the
-variation method, maximum likelihood, quantile matching and linear
regression of the empirical characteristic function
Phase transitions for the Boolean model of continuum percolation for Cox point processes
We consider the Boolean model with random radii based on Cox point processes.
Under a condition of stabilization for the random environment, we establish
existence and non-existence of subcritical regimes for the size of the cluster
at the origin in terms of volume, diameter and number of points. Further, we
prove uniqueness of the infinite cluster for sufficiently connected
environments.Comment: 22 pages, 2 figure
Around the circular law
These expository notes are centered around the circular law theorem, which
states that the empirical spectral distribution of a nxn random matrix with
i.i.d. entries of variance 1/n tends to the uniform law on the unit disc of the
complex plane as the dimension tends to infinity. This phenomenon is the
non-Hermitian counterpart of the semi circular limit for Wigner random
Hermitian matrices, and the quarter circular limit for Marchenko-Pastur random
covariance matrices. We present a proof in a Gaussian case, due to Silverstein,
based on a formula by Ginibre, and a proof of the universal case by revisiting
the approach of Tao and Vu, based on the Hermitization of Girko, the
logarithmic potential, and the control of the small singular values. Beyond the
finite variance model, we also consider the case where the entries have heavy
tails, by using the objective method of Aldous and Steele borrowed from
randomized combinatorial optimization. The limiting law is then no longer the
circular law and is related to the Poisson weighted infinite tree. We provide a
weak control of the smallest singular value under weak assumptions, using
asymptotic geometric analysis tools. We also develop a quaternionic
Cauchy-Stieltjes transform borrowed from the Physics literature.Comment: Added: one reference and few comment
A moment-matching Ferguson and Klass algorithm
Completely random measures (CRM) represent the key building block of a wide
variety of popular stochastic models and play a pivotal role in modern Bayesian
Nonparametrics. A popular representation of CRMs as a random series with
decreasing jumps is due to Ferguson and Klass (1972). This can immediately be
turned into an algorithm for sampling realizations of CRMs or more elaborate
models involving transformed CRMs. However, concrete implementation requires to
truncate the random series at some threshold resulting in an approximation
error. The goal of this paper is to quantify the quality of the approximation
by a moment-matching criterion, which consists in evaluating a measure of
discrepancy between actual moments and moments based on the simulation output.
Seen as a function of the truncation level, the methodology can be used to
determine the truncation level needed to reach a certain level of precision.
The resulting moment-matching \FK algorithm is then implemented and illustrated
on several popular Bayesian nonparametric models.Comment: 24 pages, 6 figures, 5 table
Structured Random Matrices
Random matrix theory is a well-developed area of probability theory that has
numerous connections with other areas of mathematics and its applications. Much
of the literature in this area is concerned with matrices that possess many
exact or approximate symmetries, such as matrices with i.i.d. entries, for
which precise analytic results and limit theorems are available. Much less well
understood are matrices that are endowed with an arbitrary structure, such as
sparse Wigner matrices or matrices whose entries possess a given variance
pattern. The challenge in investigating such structured random matrices is to
understand how the given structure of the matrix is reflected in its spectral
properties. This chapter reviews a number of recent results, methods, and open
problems in this direction, with a particular emphasis on sharp spectral norm
inequalities for Gaussian random matrices.Comment: 46 pages; to appear in IMA Volume "Discrete Structures: Analysis and
Applications" (Springer
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