scholarly journals The Large Deviation Principle for the On-Off Weibull Sojourn Process

2008 ◽  
Vol 45 (01) ◽  
pp. 107-117 ◽  
Author(s):  
Ken R. Duffy ◽  
Artem Sapozhnikov
Keyword(s):  
Renewal Process ◽  
Time Scale ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Rate Function ◽  
Compact Set ◽  
Natural Processes ◽  
Deviation Principle ◽  
Rate Functions ◽  
Nonlinear Scale

This article proves that the on-off renewal process with Weibull sojourn times satisfies the large deviation principle on a nonlinear scale. Unusually, its rate function is not convex. Apart from on a compact set, the rate function is infinite, which enables us to construct natural processes that satisfy the large deviation principle with nontrivial rate functions on more than one time scale.

scholarly journals The Large Deviation Principle for the On-Off Weibull Sojourn Process

2008 ◽  
Vol 45 (1) ◽  
pp. 107-117 ◽  
Author(s):  
Ken R. Duffy ◽  
Artem Sapozhnikov
Keyword(s):  
Renewal Process ◽  
Time Scale ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Rate Function ◽  
Compact Set ◽  
Natural Processes ◽  
Deviation Principle ◽  
Rate Functions ◽  
Nonlinear Scale

This article proves that the on-off renewal process with Weibull sojourn times satisfies the large deviation principle on a nonlinear scale. Unusually, its rate function is not convex. Apart from on a compact set, the rate function is infinite, which enables us to construct natural processes that satisfy the large deviation principle with nontrivial rate functions on more than one time scale.

scholarly journals Logarithmic Asymptotics for Multidimensional Extremes Under Nonlinear Scalings

2015 ◽  
Vol 52 (1) ◽  
pp. 68-81 ◽  
Author(s):  
K. M. Kosiński ◽  
M. Mandjes
Keyword(s):  
Large Deviation Principle ◽  
Large Deviation ◽  
Random Vectors ◽  
Deviation Principle ◽  
Logarithmic Asymptotics ◽  
Rate Functions ◽  
Regularly Varying ◽  
Positive Index

Let W = {Wn: n ∈ N} be a sequence of random vectors in Rd, d ≥ 1. In this paper we consider the logarithmic asymptotics of the extremes of W, that is, for any vector q > 0 in Rd, we find that logP(there exists n ∈ N: Wnuq) as u → ∞. We follow the approach of the restricted large deviation principle introduced in Duffy (2003). That is, we assume that, for every q ≥ 0, and some scalings {an}, {vn}, (1 / vn)logP(Wn / an ≥ uq) has a, continuous in q, limit JW(q). We allow the scalings {an} and {vn} to be regularly varying with a positive index. This approach is general enough to incorporate sequences W, such that the probability law of Wn / an satisfies the large deviation principle with continuous, not necessarily convex, rate functions. The equations for these asymptotics are in agreement with the literature.

scholarly journals The Large Deviations of Estimating Rate Functions

2005 ◽  
Vol 42 (01) ◽  
pp. 267-274 ◽  
Author(s):  
Ken Duffy ◽  
Anthony P. Metcalfe
Keyword(s):  
Large Deviation Principle ◽  
Large Deviation ◽  
Length Distribution ◽  
Rate Function ◽  
Partial Sums ◽  
Single Server ◽  
Mixing Condition ◽  
Deviation Principle ◽  
Queue Length Distribution ◽  
Empirical Estimates

Given a sequence of bounded random variables that satisfies a well-known mixing condition, it is shown that empirical estimates of the rate function for the partial sums process satisfy the large deviation principle in the space of convex functions equipped with the Attouch-Wets topology. As an application, a large deviation principle for estimating the exponent in the tail of the queue length distribution at a single-server queue with infinite waiting space is proved.

scholarly journals How to estimate the rate function of a cumulative process

2005 ◽  
Vol 42 (4) ◽  
pp. 1044-1052 ◽  
Author(s):  
Ken Duffy ◽  
Anthony P. Metcalfe
Keyword(s):  
Renewal Process ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Direct Method ◽  
Random Variables ◽  
Rate Function ◽  
Dirac Measure ◽  
Mixing Conditions ◽  
A Value ◽  
Cumulative Process

Consider two sequences of bounded random variables, a value and a timing process, that satisfy the large deviation principle (LDP) with rate function J(⋅,⋅) and whose cumulative process satisfies the LDP with rate function I(⋅). Under mixing conditions, an LDP for estimates of I constructed by transforming an estimate of J is proved. For the case of a cumulative renewal process it is demonstrated that this approach is favourable to a more direct method, as it ensures that the laws of the estimates converge weakly to a Dirac measure at I.

scholarly journals THE MATHER MEASURE AND A LARGE DEVIATION PRINCIPLE FOR THE ENTROPY PENALIZED METHOD

2011 ◽  
Vol 13 (02) ◽  
pp. 235-268 ◽  
Author(s):  
D. A. GOMES ◽  
A. O. LOPES ◽  
J. MOHR
Keyword(s):  
Jacobi Equation ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Rate Function ◽  
Deviation Function ◽  
Deviation Principle ◽  
Probability Densities ◽  
Aubry Set ◽  
Dynamical Properties ◽  
Hamilton Jacobi Equation

We present the rate function and a large deviation principle for the entropy penalized Mather problem when the Lagrangian is generic (it is known that in this case the Mather measure μ is unique and the support of μ is the Aubry set). We assume the Lagrangian L(x, v), with x in the torus 𝕋N and v∈ℝN, satisfies certain natural hypotheses, such as superlinearity and convexity in v, as well as some technical estimates. Consider, for each value of ϵ and h, the entropy penalized Mather problem [Formula: see text] where the entropy S is given by [Formula: see text], and the minimization is performed over the space of probability densities μ(x, v) on 𝕋N×ℝN that satisfy the discrete holonomy constraint ∫𝕋N×ℝN φ(x + hv) - φ(x) dμ = 0. It is known [17] that there exists a unique minimizing measure μϵ, h which converges to a Mather measure μ, as ϵ, h→0. In the case in which the Mather measure μ is unique we prove a Large Deviation Principle for the limit lim ϵ, h→0ϵ ln μϵ, h(A), where A ⊂ 𝕋N×ℝN. In particular, we prove that the deviation function I can be written as [Formula: see text], where ϕ0 is the unique viscosity solution of the Hamilton – Jacobi equation, [Formula: see text]. We also prove a large deviation principle for the limit ϵ→ 0 with fixed h. Finally, in the last section, we study some dynamical properties of the discrete time Aubry–Mather problem, and present a proof of the existence of a separating subaction.

scholarly journals The Large Deviations of Estimating Rate Functions

2005 ◽  
Vol 42 (1) ◽  
pp. 267-274 ◽  
Author(s):  
Ken Duffy ◽  
Anthony P. Metcalfe
Keyword(s):  
Large Deviation Principle ◽  
Large Deviation ◽  
Length Distribution ◽  
Rate Function ◽  
Partial Sums ◽  
Single Server ◽  
Mixing Condition ◽  
Deviation Principle ◽  
Queue Length Distribution ◽  
Empirical Estimates

Given a sequence of bounded random variables that satisfies a well-known mixing condition, it is shown that empirical estimates of the rate function for the partial sums process satisfy the large deviation principle in the space of convex functions equipped with the Attouch-Wets topology. As an application, a large deviation principle for estimating the exponent in the tail of the queue length distribution at a single-server queue with infinite waiting space is proved.

THE LARGE DEVIATION PRINCIPLE FOR MEASURES WITH RANDOM WEIGHTS

1993 ◽  
Vol 05 (04) ◽  
pp. 659-692 ◽  
Author(s):  
R. S. ELLIS ◽  
J. GOUGH ◽  
J. V. PULÉ
Keyword(s):  
Large Deviation Principle ◽  
Large Deviation ◽  
Spherical Model ◽  
Fermi Gas ◽  
Rate Function ◽  
Bose Statistics ◽  
Deviation Principle ◽  
Random Weights ◽  
Abstract Setting ◽  
Special Case

In this paper, we study the problem of large deviations for measures with random weights. We are motivated by previous work dealing with the special case occuring in the statistical mechanics of the Bose gas. We study the problem in an abstract setting, isolating what is general from what is dependent on Bose statistics. We succeed in proving the large deviation principle for a large class of measures with random weights and obtaining the corresponding rate function in an explicit form. In particular, our results are applicable to the Fermi gas and the spherical model.

An invariance principle and a large deviation principle for the biased random walk on

2020 ◽  
Vol 57 (1) ◽  
pp. 295-313
Author(s):  
Yuelin Liu ◽  
Vladas Sidoravicius ◽  
Longmin Wang ◽  
Kainan Xiang
Keyword(s):  
Brownian Motion ◽  
Random Walk ◽  
Invariance Principle ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Scaling Limit ◽  
Rate Function ◽  
Deviation Principle ◽  
Biased Random Walk

AbstractWe establish an invariance principle and a large deviation principle for a biased random walk ${\text{RW}}_\lambda$ with $\lambda\in [0,1)$ on $\mathbb{Z}^d$ . The scaling limit in the invariance principle is not a d-dimensional Brownian motion. For the large deviation principle, its rate function is different from that of a drifted random walk, as may be expected, though the reflected biased random walk evolves like the drifted random walk in the interior of the first quadrant and almost surely visits coordinate planes finitely many times.

scholarly journals How to estimate the rate function of a cumulative process

2005 ◽  
Vol 42 (04) ◽  
pp. 1044-1052 ◽  
Author(s):  
Ken Duffy ◽  
Anthony P. Metcalfe
Keyword(s):  
Renewal Process ◽  
Large Deviation Principle ◽  
Large Deviation ◽  
Direct Method ◽  
Random Variables ◽  
Rate Function ◽  
Dirac Measure ◽  
Mixing Conditions ◽  
A Value ◽  
Cumulative Process

Consider two sequences of bounded random variables, a value and a timing process, that satisfy the large deviation principle (LDP) with rate function J(⋅,⋅) and whose cumulative process satisfies the LDP with rate function I(⋅). Under mixing conditions, an LDP for estimates of I constructed by transforming an estimate of J is proved. For the case of a cumulative renewal process it is demonstrated that this approach is favourable to a more direct method, as it ensures that the laws of the estimates converge weakly to a Dirac measure at I.

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