Large Deviation Estimates Research Articles

An Itô Formula for an Accretive Operator

We give an Ito formula associated to a non-linear semi-group associated to a m-accretive operator.

On large deviations for Poisson stochastic integrals

We obtain asymptotically exact estimates for large deviations of Poisson stochastic integrals. We also find a region where such an integral can be approximated by the corresponding Gaussian random variable. In of all these results, we obtain nonasymptotic estimates for remainder terms.

Optimal allocation to maximize the power of two-sample tests for binary response

SUMMARY We study allocations that maximize the power of tests of equality of two treatments having binary outcomes. When a normal approximation applies, the asymptotic power is maximized by minimizing the variance, leading to a Neyman allocation that assigns observations in proportion to the standard deviations. This allocation, which in general requires knowledge of the parameters of the problem, is recommended in a large body of literature. Under contiguous alternatives the normal approximation indeed applies, and in this case the Neyman allocation reduces to a balanced design. However, when studying the power under a noncontiguous alternative, a large deviations approximation is needed, and the Neyman allocation is no longer asymptotically optimal. In the latter case, the optimal allocation depends on the parameters, but is rather close to a balanced design. Thus, a balanced design is a viable option for both contiguous and noncontiguous alternatives. Finite sample studies show that a balanced design is indeed generally quite close to being optimal for power maximization. This is good news as implementation of a balanced design does not require knowledge of the parameters.

Large and Moderate Deviations for Projective Systems and Projective Limits

One of the active fields in applied probability, the last two decades, is that of large deviations theory i.e. the one dealing with the (asymptotic) computation of probabilities of rare events which are exponentially small as a function of some parameter e.g. the amplitude of the noise perturbing a dynamical system. Basic ideas of the theory can be tracked back to Laplace, the first rigorous results are due to Cramer although a clear definition was introduced by Varadhan in 1966. Large deviations estimates have been proved to be the crucial tool in studying problems in Statistics, Physics (Thermodynamics and Statistical Mechanics), Finance (Monte-Carlo methods, option pricing, long term portfolio investment) and in Applied probability (queuing theory). The aim of this work is to describe one of the (recent) methods of proving large deviations results, namely that of projective systems. We compare the method with the one of projective limits and show the advantages of the first. These advantages are due to the fact that: 1) the arguments are direct and the proofs of the basic results of the theory are much easier and simpler; 2) we are able to extend most of these results using suitable projective systems. We apply the method in the case of a) sequences of i.i.d. r.v.’s and b) sequences of exchangeable r.v.’s. All the results are being proved in a simple “unified” way.

On the internal distance in the interlacement set

We prove a shape theorem for the internal (graph) distance on the interlacement set $\mathcal{I}^u$ of the random interlacement model on $\mathbb Z^d$, $d\ge 3$. We provide large deviation estimates for the internal distance of distant points in this set, and use these estimates to study the internal distance on the range of a simple random walk on a discrete torus.

The distribution of height and diameter in random non‐plane binary trees

AbstractThis study is dedicated to precise distributional analyses of the height of non‐plane unlabelled binary trees (“Otter trees”), when trees of a given size are taken with equal likelihood. The height of a rooted tree of size n is proved to admit a limiting theta distribution, both in a central and local sense, and obey moderate as well as large deviations estimates. The approximations obtained for height also yield the limiting distribution of the diameter of unrooted trees. The proofs rely on a precise analysis, in the complex plane and near singularities, of generating functions associated with trees of bounded height. © 2011 Wiley Periodicals, Inc. Random Struct. Alg., 2012

Large Deviation Results for Wave Governed Random Motions Driven by Semi-Markov Processes

In this article, we present large deviation results for a model {ξ1 + … + ξ n : n ≥ 1} which is close to a random walk. More precisely, we consider independent random variables {ξ n : n ≥ 1} such that {ξ n : n ≥ 2} are i.i.d. and a different distribution for ξ1 is allowed. We prove large deviation estimates for P(N x ≤ xT) and P(N x < ∞) as x → ∞, where N x : = inf {n ≥ 1: ξ1 + … + ξ n ≥ x}. Moreover, we provide an asymptotically efficient simulation law for the estimation of P(N x ≤ xT) and P(N x < ∞) by Monte Carlo simulation based on the importance sampling technique. These results will be adapted to wave governed random motions driven by semi-Markov processes and we present some simulations. Finally, we study the convergence of some large deviation rates for standard wave governed random motions based on a scaling presented in the literature (see Kac, 1974; Orsingher, 1990).

Interpolating Thin-Shell and Sharp Large-Deviation Estimates for Lsotropic Log-Concave Measures

Given an isotropic random vector X with log-concave density in Euclidean space \({\mathbb{R}^n}\) , we study the concentration properties of |X| on all scales, both above and below its expectation. We show in particular that $$\begin{array}{l} \mathbb{P}\left ( \left | |X| - \sqrt{n} \right | \geq t\sqrt{n} \right ) \leq C \, {\rm exp} \left ( -cn^{1/2} {\rm min}(t^{3}, t) \right) \; \forall t \geq 0, \end{array}$$ for some universal constants c, C > 0. This improves the best known deviation results on the thin-shell and mesoscopic scales due to Fleury and Klartag, respectively, and recovers the sharp large-deviation estimate of Paouris. Another new feature of our estimate is that it improves when X is \({\psi_{\alpha}\, (\alpha \in(1, 2])}\) , in precise agreement with Paouris’ estimates. The upper bound on the thin-shell width \({\sqrt{\mathbb{V}{\rm ar}(|X|)}}\) we obtain is of the order of n1/3, and improves down to n1/4 when X is \({\psi_{2}}\) . Our estimates thus continuously interpolate between a new best known thin-shell estimate and the sharp large-deviation estimate of Paouris. As a consequence, a new best known bound on the Cheeger isoperimetric constant appearing in a conjecture of Kannan.Lovasz-Simonovits is deduced.

The missing log in large deviations for triangle counts

AbstractThis paper solves the problem of sharp large deviation estimates for the upper tail of the number of triangles in an Erdős‐Rényi random graph, by establishing a logarithmic factor in the exponent that was missing till now. It is possible that the method of proof may extend to general subgraph counts. © 2011 Wiley Periodicals, Inc. Random Struct. Alg., 40, 437–451, 2012

Modulation spaces, Wiener amalgam spaces, and Brownian motions

We study the local-in-time regularity of the Brownian motion with respect to localized variants of modulation spaces M s p , q and Wiener amalgam spaces W s p , q . We show that the periodic Brownian motion belongs locally in time to M s p , q ( T ) and W s p , q ( T ) for ( s − 1 ) q < − 1 , and the condition on the indices is optimal. Moreover, with the Wiener measure μ on T , we show that ( M s p , q ( T ) , μ ) and ( W s p , q ( T ) , μ ) form abstract Wiener spaces for the same range of indices, yielding large deviation estimates. We also establish the endpoint regularity of the periodic Brownian motion with respect to a Besov-type space b ˆ p , ∞ s ( T ) . Specifically, we prove that the Brownian motion belongs to b ˆ p , ∞ s ( T ) for ( s − 1 ) p = − 1 , and it obeys a large deviation estimate. Finally, we revisit the regularity of Brownian motion on usual local Besov spaces B p , q s , and indicate the endpoint large deviation estimates.

Large Deviation Bounds for Functionals of Viterbi Paths

In a number of applications, the underlying stochastic process is modeled as a finite-state discrete-time Markov chain that cannot be observed directly and is represented by an auxiliary process. The maximum a posteriori (MAP) estimator is widely used to estimate states of this hidden Markov model through available observations. The MAP path estimator based on a finite number of observations is calculated by the Viterbi algorithm, and is often referred to as the Viterbi path. It was recently shown in, and, (see also and) that under mild conditions, the sequence of estimators of a given state converges almost surely to a limiting regenerative process as the number of observations approaches infinity. This in particular implies a law of large numbers for some functionals of hidden states and finite Viterbi paths. The aim of this paper is to provide the corresponding large deviation estimates.

Some large deviation estimates for an Erlang queue of phosphorylated T-cell receptors

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.

General Freidlin–Wentzell Large Deviations and positive diffusions

We prove Freidlin–Wentzell Large Deviation estimates under rather minimal assumptions. This allows one to derive Wentzell–Freidlin Large Deviation estimates for diffusions on the positive half line with coefficients that are neither bounded nor Lipschitz continuous. This applies to models of interest in Finance, i.e. the CIR and the CEV models, which are positive diffusion processes whose diffusion coefficient is only Hölder continuous.

Large deviations for local times and intersection local times of fractional Brownian motions and Riemann–Liouville processes

In this paper we prove exact forms of large deviations for local times and intersection local times of fractional Brownian motions and Riemann-Liouville processes. We also show that a fractional Brownian motion and the related Riemann-Liouville process behave like constant multiples of each other with regard to large deviations for their local and intersection local times. As a consequence of our large deviation estimates, we derive laws of iterated logarithm for the corresponding local times. The key points of our methods: (1) logarithmic superadditivity of a normalized sequence of moments of exponentially randomized local time of a fractional Brownian motion; (2) logarithmic subadditivity of a normalized sequence of moments of exponentially randomized intersection local time of Riemann-Liouville processes; (3) comparison of local and intersection local times based on embedding of a part of a fractional Brownian motion into the reproducing kernel Hilbert space of the Riemann-Liouville process.

Spectral analysis of the transfer operator for the Lorentz gas

We study the billiard map associated with both the finite- andinfinite-horizon Lorentz gases having smooth scatterers with strictlypositive curvature. We introduce generalized function spaces (Banach spacesof distributions) on which the transfer operator is quasicompact. Themixing properties of the billiard map then imply the existence of aspectral gap and related statistical properties such as exponential decayof correlations and the Central Limit Theorem. Finer statistical propertiesof the map such as the identification of Ruelle resonances, large deviationestimates and an almost-sure invariance principle follow immediately oncethe spectral picture is established.

Stochastic Analysis of the Motion of DNA Nanomechanical Bipeds

In this paper, we formulate and analyze a Markov process modeling the motion of DNA nanomechanical walking devices.We consider a molecular biped restricted to a well-defined one-dimensional track and study its asymptotic behavior.Our analysis allows for the biped legs to be of different molecular composition, and thus to contribute differently to the dynamics. Our main result is a functional central limit theorem for the biped with an explicit formula for the effective diffusivity coefficient in terms of the parameters of the model. A law of large numbers, a recurrence/transience characterization and large deviations estimates are also obtained.Our approach is applicable to a variety of other biological motors such as myosin and motor proteins on polymer filaments.

Risk processes with shot noise Cox claim number process and reserve dependent premium rate

We consider a suitable scaling, called the slow Markov walk limit, for a risk process with shot noise Cox claim number process and reserve dependent premium rate. We provide large deviation estimates for the ruin probability. Furthermore, we find an asymptotically efficient law for the simulation of the ruin probability using importance sampling. Finally, we present asymptotic bounds for ruin probabilities in the Bayesian setting.

Large deviations for estimators of unknown probabilities, with applications in risk theory

We present large deviation results for estimators of unknown probabilities which satisfy a suitable exponential decay condition. These results provide some extensions of the large deviation estimates given in Macci and Petrella (2006). Furthermore we propose a classical approach which is different from the one presented in Ganesh et al. (1998) and we cannot say that the Bayesian approach is more conservative as in that paper.

Intermittency on catalysts: Voter model

In this paper we study intermittency for the parabolic Anderson equation $\partial u/\partial t=\kappa\Delta u+\gamma\xi u$ with $u:\mathbb{Z}^d\times[0,\infty)\to\mathbb{R}$, where $\kappa\in[0,\infty)$ is the diffusion constant, $\Delta$ is the discrete Laplacian, $\gamma\in(0,\infty)$ is the coupling constant, and $\xi:\mathbb{Z}^d\times[0,\infty)\to\mathbb{R}$ is a space--time random medium. The solution of this equation describes the evolution of a ``reactant'' $u$ under the influence of a ``catalyst'' $\xi$. We focus on the case where $\xi$ is the voter model with opinions 0 and 1 that are updated according to a random walk transition kernel, starting from either the Bernoulli measure $\nu_{\rho}$ or the equilibrium measure $\mu_{\rho}$, where $\rho\in(0,1)$ is the density of 1's. We consider the annealed Lyapunov exponents, that is, the exponential growth rates of the successive moments of $u$. We show that if the random walk transition kernel has zero mean and finite variance, then these exponents are trivial for $1\leq d\leq4$, but display an interesting dependence on the diffusion constant $\kappa$ for $d\geq 5$, with qualitatively different behavior in different dimensions. In earlier work we considered the case where $\xi$ is a field of independent simple random walks in a Poisson equilibrium, respectively, a symmetric exclusion process in a Bernoulli equilibrium, which are both reversible dynamics. In the present work a main obstacle is the nonreversibility of the voter model dynamics, since this precludes the application of spectral techniques. The duality with coalescing random walks is key to our analysis, and leads to a representation formula for the Lyapunov exponents that allows for the application of large deviation estimates.

Total progeny in killed branching random walk

We consider a branching random walk for which the maximum position of a particle in the n’th generation, Rn, has zero speed on the linear scale: Rn/n → 0 as n → ∞. We further remove (“kill”) any particle whose displacement is negative, together with its entire descendence. The size Z of the set of un-killed particles is almost surely finite (Gantert and Muller in Markov Process. Relat. Fields 12:805–814, 2006; Hu and Shi in Ann. Probab. 37(2):742–789, 2009). In this paper, we confirm a conjecture of Aldous (Algorithmica 22:388–412, 1998; and Power laws and killed branching random walks) that E [Z] < ∞ while \({{\mathbf E}\left[Z\,{\rm log}\, Z\right]=\infty}\) . The proofs rely on precise large deviations estimates and ballot theorem-style results for the sample paths of random walks.