671 research outputs found
Expected Supremum of a Random Linear Combination of Shifted Kernels
We address the expected supremum of a linear combination of shifts of the
sinc kernel with random coefficients. When the coefficients are Gaussian, the
expected supremum is of order \sqrt{\log n}, where n is the number of shifts.
When the coefficients are uniformly bounded, the expected supremum is of order
\log\log n. This is a noteworthy difference to orthonormal functions on the
unit interval, where the expected supremum is of order \sqrt{n\log n} for all
reasonable coefficient statistics.Comment: To appear in the Journal of Fourier Analysis and Application
Sampling in a Quantum Population, and Applications
We propose a framework for analyzing classical sampling strategies for estimating the Hamming weight of a large string, when applied to a multi-qubit quantum system instead. The framework shows how to interpret such a strategy and how to define its accuracy when applied to a quantum system. Furthermore, we show how the accuracy of any strategy relates to its accuracy in its classical usage, which is well understood for the important examples. We show the usefulness of our framework by using it to obtain new and simple security proofs for the following quantum-cryptographic schemes: quantum oblivious-transfer from bit-commitment, and BB84 quantum-key-distribution
Error Exponent in Asymmetric Quantum Hypothesis Testing and Its Application to Classical-Quantum Channel coding
In the simple quantum hypothesis testing problem, upper bound with asymmetric
setting is shown by using a quite useful inequality by Audenaert et al,
quant-ph/0610027, which was originally invented for symmetric setting. Using
this upper bound, we obtain the Hoeffding bound, which are identical with the
classical counter part if the hypotheses, composed of two density operators,
are mutually commutative. Our upper bound improves the bound by Ogawa-Hayashi,
and also provides a simpler proof of the direct part of the quantum Stein's
lemma. Further, using this bound, we obtain a better exponential upper bound of
the average error probability of classical-quantum channel coding
Derandomized Construction of Combinatorial Batch Codes
Combinatorial Batch Codes (CBCs), replication-based variant of Batch Codes
introduced by Ishai et al. in STOC 2004, abstracts the following data
distribution problem: data items are to be replicated among servers in
such a way that any of the data items can be retrieved by reading at
most one item from each server with the total amount of storage over
servers restricted to . Given parameters and , where and
are constants, one of the challenging problems is to construct -uniform CBCs
(CBCs where each data item is replicated among exactly servers) which
maximizes the value of . In this work, we present explicit construction of
-uniform CBCs with data items. The
construction has the property that the servers are almost regular, i.e., number
of data items stored in each server is in the range . The
construction is obtained through better analysis and derandomization of the
randomized construction presented by Ishai et al. Analysis reveals almost
regularity of the servers, an aspect that so far has not been addressed in the
literature. The derandomization leads to explicit construction for a wide range
of values of (for given and ) where no other explicit construction
with similar parameters, i.e., with , is
known. Finally, we discuss possibility of parallel derandomization of the
construction
Iterated Jackknives and Two-Sided Variance Inequalities
We consider the variance of a function of independent random variables
and provide new inequalities which, in particular, extend previous results
obtained for symmetric functions in the i.i.d.~setting. For instance, we obtain
various upper and lower variance bounds based on iterated jackknives statistics
that can be considered as generalizations of the Efron-Stein inequality.Comment: This paper has appeared in High Dimensional Probability VIII, Part of
the Progress in Probability book series (PRPR, volume 74), Pages 33-40. Some
typos have been corrected and slight corrections mad
PAC-Bayesian Bounds for Randomized Empirical Risk Minimizers
The aim of this paper is to generalize the PAC-Bayesian theorems proved by
Catoni in the classification setting to more general problems of statistical
inference. We show how to control the deviations of the risk of randomized
estimators. A particular attention is paid to randomized estimators drawn in a
small neighborhood of classical estimators, whose study leads to control the
risk of the latter. These results allow to bound the risk of very general
estimation procedures, as well as to perform model selection
Optimal Uncertainty Quantification
We propose a rigorous framework for Uncertainty Quantification (UQ) in which
the UQ objectives and the assumptions/information set are brought to the
forefront. This framework, which we call \emph{Optimal Uncertainty
Quantification} (OUQ), is based on the observation that, given a set of
assumptions and information about the problem, there exist optimal bounds on
uncertainties: these are obtained as values of well-defined optimization
problems corresponding to extremizing probabilities of failure, or of
deviations, subject to the constraints imposed by the scenarios compatible with
the assumptions and information. In particular, this framework does not
implicitly impose inappropriate assumptions, nor does it repudiate relevant
information. Although OUQ optimization problems are extremely large, we show
that under general conditions they have finite-dimensional reductions. As an
application, we develop \emph{Optimal Concentration Inequalities} (OCI) of
Hoeffding and McDiarmid type. Surprisingly, these results show that
uncertainties in input parameters, which propagate to output uncertainties in
the classical sensitivity analysis paradigm, may fail to do so if the transfer
functions (or probability distributions) are imperfectly known. We show how,
for hierarchical structures, this phenomenon may lead to the non-propagation of
uncertainties or information across scales. In addition, a general algorithmic
framework is developed for OUQ and is tested on the Caltech surrogate model for
hypervelocity impact and on the seismic safety assessment of truss structures,
suggesting the feasibility of the framework for important complex systems. The
introduction of this paper provides both an overview of the paper and a
self-contained mini-tutorial about basic concepts and issues of UQ.Comment: 90 pages. Accepted for publication in SIAM Review (Expository
Research Papers). See SIAM Review for higher quality figure
Stochastic Flux-Freezing and Magnetic Dynamo
We argue that magnetic flux-conservation in turbulent plasmas at high
magnetic Reynolds numbers neither holds in the conventional sense nor is
entirely broken, but instead is valid in a novel statistical sense associated
to the "spontaneous stochasticity" of Lagrangian particle tra jectories. The
latter phenomenon is due to the explosive separation of particles undergoing
turbulent Richardson diffusion, which leads to a breakdown of Laplacian
determinism for classical dynamics. We discuss empirical evidence for
spontaneous stochasticity, including our own new numerical results. We then use
a Lagrangian path-integral approach to establish stochastic flux-freezing for
resistive hydromagnetic equations and to argue, based on the properties of
Richardson diffusion, that flux-conservation must remain stochastic at infinite
magnetic Reynolds number. As an important application of these results we
consider the kinematic, fluctuation dynamo in non-helical, incompressible
turbulence at unit magnetic Prandtl number. We present results on the
Lagrangian dynamo mechanisms by a stochastic particle method which demonstrate
a strong similarity between the Pr = 1 and Pr = 0 dynamos. Stochasticity of
field-line motion is an essential ingredient of both. We finally consider
briefly some consequences for nonlinear MHD turbulence, dynamo and reconnectionComment: 29 pages, 10 figure
Affine equivariant rank-weighted L-estimation of multivariate location
In the multivariate one-sample location model, we propose a class of flexible
robust, affine-equivariant L-estimators of location, for distributions invoking
affine-invariance of Mahalanobis distances of individual observations. An
involved iteration process for their computation is numerically illustrated.Comment: 16 pages, 4 figures, 6 table
An Edgeworth expansion for finite population L-statistics
In this paper, we consider the one-term Edgeworth expansion for finite
population L-statistics. We provide an explicit formula for the Edgeworth
correction term and give sufficient conditions for the validity of the
expansion which are expressed in terms of the weight function that defines the
statistics and moment conditions.Comment: 14 pages. Minor revisions. Some explanatory comments and a numerical
example were added. Lith. Math. J. (to appear
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