Loynes’ distribution, which characterizes the one dimensional marginal of the stationary solution to Lindley’s recursion, possesses an ultimately exponential tail for a large class of increment processes. If one can observe increments but does not know their probabilistic properties, what are the statistical limits of estimating the tail exponent of Loynes’ distribution? We conjecture that in broad generality a consistent sequence of non-parametric estimators can be constructed that satisfies a large deviation principle. We present rigorous support for this conjecture under restrictive assumptions and simulation evidence indicating why we believe it to be true in greater generality
Let X be a Levy process with regularly varying Levy measure ν. We obtain sample-path large deviation...
This work is a contribution towards relaxing independence in the theory of LDP. Section 2 of this pa...
Consider a random sample from a statistical model with an unknown, and possibly infinite-dimensional...
Loynes’ distribution, which characterizes the one dimensional marginal of the stationary solution t...
Given a sequence of bounded random variables that satisfies a well known mixing condition, it is sh...
A large deviations approach to the statistics of extreme events addresses the statistical analysis o...
The main purpose of the article is to provide a simpler and more elementary alternative derivation o...
Summarization: Let {Xj}j=1∞ be a sequence of r.v.'s defined on a probability space (Ω,F,μ) and takin...
We investigate the tail behaviour of the steady state distribution of a stochastic recursion that ge...
... exponent in the tail of a queue-length distribution at a single server queue with infinite waiti...
We investigate the tail behaviour of the steady state distribution of a stochastic recursion that ge...
AbstractIn this paper, we state a large deviation principle (LDP) and sharp LDP for maximum likeliho...
In this paper we consider several examples of sequences of partial sums of triangular arrays of ran...
We use process level large deviation analysis to obtain the rate function for a general family of oc...
Let {Xk, k ≥ 1} be a sequence of independent, identically distributed nonnegative random variables w...
Let X be a Levy process with regularly varying Levy measure ν. We obtain sample-path large deviation...
This work is a contribution towards relaxing independence in the theory of LDP. Section 2 of this pa...
Consider a random sample from a statistical model with an unknown, and possibly infinite-dimensional...
Loynes’ distribution, which characterizes the one dimensional marginal of the stationary solution t...
Given a sequence of bounded random variables that satisfies a well known mixing condition, it is sh...
A large deviations approach to the statistics of extreme events addresses the statistical analysis o...
The main purpose of the article is to provide a simpler and more elementary alternative derivation o...
Summarization: Let {Xj}j=1∞ be a sequence of r.v.'s defined on a probability space (Ω,F,μ) and takin...
We investigate the tail behaviour of the steady state distribution of a stochastic recursion that ge...
... exponent in the tail of a queue-length distribution at a single server queue with infinite waiti...
We investigate the tail behaviour of the steady state distribution of a stochastic recursion that ge...
AbstractIn this paper, we state a large deviation principle (LDP) and sharp LDP for maximum likeliho...
In this paper we consider several examples of sequences of partial sums of triangular arrays of ran...
We use process level large deviation analysis to obtain the rate function for a general family of oc...
Let {Xk, k ≥ 1} be a sequence of independent, identically distributed nonnegative random variables w...
Let X be a Levy process with regularly varying Levy measure ν. We obtain sample-path large deviation...
This work is a contribution towards relaxing independence in the theory of LDP. Section 2 of this pa...
Consider a random sample from a statistical model with an unknown, and possibly infinite-dimensional...