Moderate deviation for maximum likelihood estimators from single server queues

Consider a single server queueing model which is observed over a continuous time interval (0, T ], where T is determined by a suitable stopping rule. Let θ be the unknown parameter for the arrival process and θ̂T be the maximum likelihood estimator of θ . The main goal of this paper is to obtain a moderate deviation result of the maximum likelihood estimator for the single server queueing model under certain regular conditions.


Introduction
Statistical analysis on queueing theory has come a long way in the past sixty years.
A key component for the estimation of queueing parameter is maximum likelihood estimation. The problem of estimation of the unknown parameter using maximum likelihood estimation has been discussed in the literature over the last several years. The first theoretical treatment of the estimation problem was given by Clarke (1957), who derived maximum likelihood estimates of arrival and service rates in an M/M/1 queueing system. Billingsley's (1961) treatment of inference in Markov processes in general and Wolff's (1965) derivation of likelihood ratio tests and maximum likelihood estimates for queues that can be modeled as birth and death processes are other S.K. Singh ( ) P. G. Department of Statistics, Sambalpur University, Odisha, India e-mail: sarojasngh@gmail.com significant advances in this area. The papers by Cox (1965) and Goyal and Harris (1972) are worth mentioning. Since then. significant progress has occurred in adapting statistical procedures to various systems. Prabhu (1981, 1988) and Acharya (1999) studied the asymptotic inference for single server queues, proving the consistency and asymptotic normality and finding the rate of convergence of maximum likelihood estimators in the queue GI /G/1, respectively. Recently, Acharya and Singh (2019) studied the asymptotic properties of the maximum likelihood estimator from single server queues using the martingale technique. Singh and Acharya (2019) discussed the bound for the equivalence of the Bayes and maximum likelihood estimator and also obtained the bound on the difference between the Bayes estimator from their true values of arrival and service rate parameter in an M/M/1 queue.
There has been recent interest to study the rate of convergence of the maximum likelihood estimator. Gao (2001) proved the results on moderate deviations for the maximum likelihood estimator for the case of independent and identically distributed observations and Xiao and Liu (2006) for the case of independent but not identically distributed observations. Miao and Chen (2010) gave a simpler proof to obtain these results under weaker conditions using Gärtner-Ellis theorem (cf. Dembo and Zeitouni (1998), Theorem 2.3.6, page 44). Miao and Wang (2014) improved the result in Miao and Chen (2010) by weakening the exponential integrability condition.
Our main aim in this paper is to study the problem of moderate deviations for the maximum likelihood estimator for single server GI /G/1 queueing model. Section 2 discusses the model of our interest and some elements of the maximum likelihood estimator. The main results are given in Section 3. In Section 4, we provide examples to illustrate our results.

GI /G/1 queueing model
Consider a single server queueing system in which the interarrival times {u k , k ≥ 1} and the service times {v k , k ≥ 1} are two independent sequences of independent and identically distributed nonnegative random variables with densities f (u; θ) and g(v; φ), respectively, where θ and φ are unknown parameters. Let us assume that f and g belong to the continuous exponential families given by and , respectively, the means and variances of the interarrival time and service time which are supposed to be finite. Here, dashes denotes the derivative with respect to parameters.
For simplicity, we assume that the initial customer arrives at time t = 0. Our sampling scheme is to observe the system over a continuous time interval where T is a suitable stopping time. The sample data consists of where A(T ) is the number of arrivals and D(T ) is the number of departures during (0, T ]. Obviously, no arrivals occur during Some possible stopping rules to determine T are given below: Rule 1. Observe the system until a fixed time t. Here, T = t with probability one and A(T ) and D(T ) are both random variables. Rule 2. Observe the system until d departures have occurred so that D(T ) = d.
Here, T = γ (T ) + v 1 + v 2 + · · · + v d and A(T ) are random variables. Rule 3. Observe the system until m arrivals take place so that A(T ) = m. Here, T = u 1 + u 2 + u 3 + · · · + u m and D(T ) are random variables. Under rule 4, we stop either with an arrival or in a departure. If we stop with an arrival, then The likelihood function based on data (3) is given by where F and G are distribution functions corresponding to the densities f and g, respectively. The likelihood function L T (θ, φ) remains valid under all the stopping rules.
The approximate likelihood L a T (θ, φ) is defined as The maximum likelihood estimates obtained from (5) are asymptotically equivalent to those obtained from (4) provided that the following two conditions are satisfied for T → ∞: The implications of these conditions have been explained by Basawa and Prabhu (1988). From (5), we have the log-likelihood function

Main results
In this section, we study the problem of moderate deviation, i.e., the rate of convergence of the probability P θ (λ(T )|θ T and Furthermore, Theorem 2 Let conditions (C1) to (C4) hold, then, for any closed subset F ⊂ , we have and, for any open subset G ⊂ , and, for any ε > 0, To prove the above theorems, we need the following key lemma.

Proof of Theorem 2 For a fixed close subset
Next, for a given open subset G ⊂ ⊂ R, for ∀ω ∈ G, ∀ε > 0 such that (ω − ε, ω + ε) ⊂ G, we have From Eq. (6), we have It is easy to see that the sequence {S A(T ) , A(T ) ≥ 1} is a square integrable martingale with zero mean. Using the martingale central limit theorem (see Hall and Heyde (1980)), we have for some γ θ > 0. Using similar arguments as in the Proof of Theorem 1, we have, ∀x > 0 and ∀η > 0, Now, letting η → 0 first, then x → ∞, and finally ε → 0, we get which completes the proof, since we have chosen an arbitrary ω in G in the above discussion.

M/M/1 queue
Let us consider the simplest of the queueing models used in practice, that is, an M/M/1 queue. The arrivals are assumed to occur in a Poisson process with rate θ and the service time distribution follows exponential distribution with mean 1/φ. Here, f (u, θ ) = θe −θu and g(v, φ) = φe −φv .