Consider a sample from an exponential distribution with unknown parameter θ From the sample, we wish to estimate the entire distribution, (Borel sets). If an estimator is used for , we are concerned with proximity of to , under total variation distance, the maximum likelihood estimate of θ, define , where , the 1 – (a/2) percentile from the standard normal. Then, . The preceding approximation to the 1 — α percentile of is very accurate even for small n We also consider the problem of obtaining a confidence band for the survival function, possessing minimal maximum width. Finally, a class of estimators of is compared to the maximum likelihood estimator from the viewpoint of total variation distance loss function.

Al-Osh and Alzaid (1988, Statistical Papers 29: 281–300) introduced a class of Poisson moving-average processes. In this paper, we analyze certain properties of such models. In particular, we show that the model has the property of time reversibility. Regression properties of the model are also given. Furthermore, we determine the asymptotic limit distribution for the sample autocovariance function and establish the asymptotic validity of a bootstrap approximation.

Stochastic automata networks (SANs) have recently received much attention in the literature as a means to analyze complex Markov chains in an efficient way. The main advantage of SANs over most other paradigms is that they allow a very compact description of the generator matrix by means of much smaller matrices for single automata. This representation can be exploited in different iterative techniques to compute the stationary solution. However, the set of applicable solution methods for SANs is restricted, because a solution method has to respect the specific representation of the generator matrix to exploit the compact representation. In particular, aggregation/disaggregation (a/d) methods cannot be applied in their usual realization for SANs without losing the possibility to exploit the compact representation of the generator matrix.

In this paper, a new a/d algorithm for SANs is introduced. The algorithm differs significantly from standard a/d methods because the parts to be aggregated are defined in a completely different way, exploiting the structure of the generator matrix of a SAN. Aggregation is performed with respect to single automata or sets of automata, which are the basic parts generating a SAN. It is shown that the new algorithm is efficient even if the automata are not loosely coupled.

A hunter hunts over a planning horizon t with i bullets. The distribution of the value of targets appearing and the probability of a bullet hitting are known. Encountering a target of value w, he has a choice of retiring from hunting completely, shooting a single bullet, or moving on to the next period to find another target. If he retires, he immediately obtains a terminal reward dependent on the remaining periods and bullets. If he fires and succeeds, he obtains the reward w and decides whether to retire or move on to the next epoch. If he misses and the target remains stationary, he must immediately decide whether to fire an additional bullet, retire, or move on to the next period. The objective in this paper is to examine the optimal decision rules that would maximize the total expected reward. We reveal that the optimal decision rule for firing is not always monotone in the number of remaining bullets and the optimal decision rule for retirement may become possibly the following form. It is better to continue hunting than retire, only if the hunter has neither too few bullets nor too many.

The controlled Mx/G/1-type queueing model with two modes of operation is considered. The modes are characterized by different service time distributions and input rates. The switchover times are imposed in the model. The embedded stationary queue-length distribution and the explicit dependence of operation criteria on switchover levels are derived.

A single server processes jobs that can yield rewards but expire on predetermined dates. Expected immediate rewards from each job are deteriorating. The instance is formulated as a multiarmed bandit problem, and an index-based scheduling policy is shown to maximize the expected total reward.

A two–time scale stochastic approximation algorithm is proposed for simulation-based parametric optimization of hidden Markov models, as an alternative to the traditional approaches to “infinitesimal perturbation analysis.” Its convergence is analyzed, and a queueing example is presented.

The blood bank system is a typical example of a perishable inventory system. The commodity arrival and customer demand processes are stochastic. However, the stored items have a constant lifetime. In this study, we introduce a diffusion approximation to this system. The stock level is represented by the amount of items arriving during the age of the oldest item; it is assumed to fluctuate as an alternating two-sided regulated Brownian motion between barriers 0 and 1. Hittings of level 0 are outdatings and hittings of level 1 are unsatisfied demands. Also, there are two predetermined switchover levels, a and b, with 0 ≤ a < b ≤ 1. Whenever the stock level process upcrosses level b, the controller generates a switch in the drift from γ = γ0 to γ = γ1, while downcrossings of level a generate switches from γ1 to γ0. A useful martingale is introduced for analyzing the stationary law of the controlled process as well as the total expected discounted cost.

An efficient simulation estimator of the expected time until all states of a finite state semi-Markov process have been visited is determined.

In the theory of multivariate statistics, it is well known that given a sample of n independent p-variate normally distributed random vectors with a common variance-covariance matrix, if at least one of the n vectors has nonzero means, then the sum of squares about the sample mean of the n vectors has a noncentral Wishart distribution. However, a detailed proof for this known result is rarely found in literature. In this paper, we present a formal and complete proof for the well-known result together with an example of its applications.

In the standard (S – 1, S) stock model, demand follows a Poisson process. It has appeared to many stock analysts that this model causes an abundance of stock in reality. In case demand is caused by failure or is derived from another process, demand typically does not follow a Poisson process. In this paper, we discuss the (S – 1, S) stock model where demand follows a renewal process and the lead time is deterministic. Moreover, we will extend this to compound renewal demand and multi-echelon inventory systems. Our goal is to show the severe influence of taking the Poisson process for granted.

We consider preemptive and nonpreemptive scheduling of partially ordered tasks on parallel processors, where the precedence relations have an intervalorder, an in-forest, or a uniform out-forest structure. Processing times of tasks are random variables with an increasing in likelihood ratio distribution in the nonpreemptive case and an exponential distribution in the preemptive case. We consider a general cost that is a function of time and of the uncompleted tasks and show that the most successors (MS) policy stochastically minimizes the cost function when it satisfies certain agreeability conditions. A consequence is that the MS policy stochastically minimizes makespan, weighted flowtime, and the weighted number of late jobs.

Consider a population having size X(t) at time t that undergoes a sequence of periods of linear birth, death, and immigration. This paper provides a general method for calculating the absolute probabilities pi, (t) = P(X(t) = i) at any stage in this sequence. In general, the different periods of birth, death, and immigration will have different parameters, although some special cases are also considered where certain parameters are common to all periods. Applications of this model can be found in fiber optics when considering a general cascade of erbium-doped fiber amplifiers. In this application, population size represents photon numbers, transmission through fibers causes attenuation (death), and amplification can be described by a linear birth, death, and immigration process.

Simple optimal policies are known for the problem of scheduling jobs to minimize expected makespan on two parallel machines when the job running-time distribution has a monotone hazard rate. But no such policy appears to be known in general. We investigate the general problem by adopting two-point running-time distributions, the simplest discrete distributions not having monotone hazard rates. We derive a policy that gives an explicit, compact solution to this problem and prove its optimality. We also comment briefly on first-order extensions of the model, but each of these seems to be markedly more difficult to analyze.

The purpose of this paper is to study definitions and characterizations of orders based on reliability measures related with the doubly truncated random variable X[x, y] = (X|x ≤ X ≤ y). The relationship between these orderings and various existing orderings of life distributions are discussed. Moreover, we give two new characterizations of the likelihood ratio order based on double truncation. These new orders complete a general diagram between orders defined from truncation.

We consider a single-item, periodic-review inventory model with uncertain demands in which each period's production volume is limited by a capacity level. The demand distributions, capacity levels, and cost parameters vary according to a periodic pattern. We prove that modified base-stock policies are optimal for the finite-horizon planning model and for both the infinite-horizon discounted and undiscounted cost criterion. We further show that the optimal base-stock levels can be calculated via a simple but efficient value-iteration method. Finally, we have conducted a comprehensive numerical study to ascertain the efficiency of this solution method as well as various qualitative properties of the performance of capacitated production/inventory systems under periodically varying demand and cost patterns.