Two problems are considered: (1) The expected waiting time for the partial sums of i.i.d. positive integer-valued random variables to be a multiple of k for the first time. For aperiodic distributions this expected value is shown to equal k. The periodic case is considered, as well as the waiting time for the partial sums to equal r modulo k. (2) For two independent sequences of partial sums of positive i.i.d. random variables, the expected value of the smallest common partial sum is derived. If both distributions are aperiodic, this expected value is the product of the two means. The periodic case is considered, as well as the case of more than two sequences.

In this paper, we discuss the ordering properties of sample ranges arising from multiple-outlier exponential and proportional hazard rate (PHR) models. The purpose of this paper is twofold. First, sufficient conditions on the parameter vectors are provided for the reversed hazard rate order and the usual stochastic order between the sample ranges arising from multiple-outlier exponential models with common sample size. Next, stochastic comparisons are separately carried out for sample ranges arising from multiple-outlier exponential and PHR models with different sample sizes as well as different hazard rates. Some numerical examples are also presented to illustrate the results established here.

We give conditions on the optimality of an index policy for multiarmed bandits when arms expire independently. We also give a new simple proof of the optimality of the Gittins index policy for the classic multiarmed bandit problem.

This article shows that very accurate accurate approximations to performance measures in the multiserver M/D/c/c+N queue with finite buffer and deterministic service times can be obtained by replacing the deterministic service time by a two-phase process with exponential sojourn times and branching probabilities outside the interval [0, 1].

A two-unit parallel system with cold standby, Markovian degradation of the working unit and one repair facility is considered. The calculation of the availability of this system under a control-limit repair policy is discussed. Iterative schemes for computing the availability in case of Erlangian repair-time distributions are presented along with numerical results.

Bidimensional processes defined by dx(t) = ρ(x, y) dt and dy(t) = f(y) dt + σ(y) dW(t), where W(t) is a Wiener process, are considered. Let T(x, y, ξ) = inf[t ≥ 0: x(t] = ξ| x(0) = x, y(0) = y). Explicit expressions for the moment generating function of T(x, y, 0) and for the characteristic function of y(T(x, y, ξ)) are obtained in two special cases. The method of similarity solutions is used. Applications to optimal control problems are presented.

We investigate the sensitivity of the leaky-bucket filter, which is known as a traffic-shaping mechanism of source traffic entering an ATM network. Adopting the gradual input source model and using the infinitesimal perturbation analysis, we derive the strongly consistent estimators for the gradients of mean cell delay and a smoothness index of output process with respect to the design parameters of the system. Simulation results show the validity of our estimators.

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.

The purpose of this paper is to analyze the so-called back-off technique of the IEEE 802.11 protocol in broadcast mode with waiting queues. In contrast to existing models, packets arriving when a station (or node) is in back-off state are not discarded, but are stored in a buffer of infinite capacity. As in previous studies, the key point of our analysis hinges on the assumption that the time on the channel is viewed as a random succession of transmission slots (whose duration corresponds to the length of a packet) and mini-slots during which the back-off of the station is decremented. These events occur independently, with given probabilities. The state of a node is represented by a two-dimensional Markov chain in discrete-time, formed by the back-off counter and the number of packets at the station. Two models are proposed both of which are shown to cope reasonably well with the physical principles of the protocol. The stability (ergodicity) conditions are obtained and interpreted in terms of maximum throughput. Several approximations related to these models are also discussed.

In this article, we discuss some new upper and lower bounds for the survivor function of the sum of n independent random variables each of which has an NBUE (new better than used in expectation) distribution. In some cases, only the means of the random variables are assumed known. These bounds are compared to the sharp bounds given in Cheng and Lam [6], which requires both means and variances known. Although the new bounds are not sharp, they often produce better upper bounds for the survivor function in the extreme right tail of many NBUE lifetime distributions, an important special case in applications. Moreover, a lower bound exists in one case not handled by the lower bounds of Theorem 3 in Cheng and Lam [6]. Numerical studies are presented along with theoretical discussions.

We study the problem of broadcasting in a system where nodes are equipped with radio transmitters with constant radius of transmission. A message originating at a node has to be transmitted to all the other nodes in the system. We prove the central limit theorem and the law of large numbers for the number of time steps required to complete a broadcast for the case when the nodes are placed on a line independently uniformly distributed. We show that the number of time steps required to broadcast is 3n/4 in probability.

We propose a novel time discretization for the log-normal SABR model which is a popular stochastic volatility model that is widely used in financial practice. Our time discretization is a variant of the Euler–Maruyama scheme. We study its asymptotic properties in the limit of a large number of time steps under a certain asymptotic regime which includes the case of finite maturity, small vol-of-vol and large initial volatility with fixed product of vol-of-vol and initial volatility. We derive an almost sure limit and a large deviations result for the log-asset price in the limit of a large number of time steps. We derive an exact representation of the implied volatility surface for arbitrary maturity and strike in this regime. Using this representation, we obtain analytical expansions of the implied volatility for small maturity and extreme strikes, which reproduce at leading order known asymptotic results for the continuous time model.

In this paper, a new point process is introduced. It combines the nonhomogeneous Poisson process with the generalized Polya process (GPP) studied in recent literature. In reliability interpretation, each event (failure) from this process is minimally repaired with a given probability and GPP-repaired with the complementary probability. Characterization of the new process via the corresponding bivariate point process is presented. The mean numbers of events for marginal processes are obtained via the corresponding rates, which are used for considering an optimal replacement problem as an application.

Signature theory plays an important part in the field of reliability. In this paper, the ordered multi-state system signature and its related properties are discussed based on a life-test of independent and non-identical coherent or mixed systems with independent and identical binary-state components. Dynamic properties of these systems are considered through a new notion called dynamic multi-state system signature, and then related comparisons are made based on system lifetimes and costs. Finally, the theoretical results established are illustrated with some specific examples to demonstrate the use of dynamic ordered multi-state system signature in evaluating used multi-state coherent or mixed systems.

We consider the problem of group testing (pooled testing), first introduced by Dorfman. For nonadaptive testing strategies, we refer to a nondefective item as “intruding” if it only appears in positive tests. Such items cause misclassification errors in the well-known COMP algorithm and can make other algorithms produce an error. It is therefore of interest to understand the distribution of the number of intruding items. We show that, under Bernoulli matrix designs, this distribution is well approximated in a variety of senses by a negative binomial distribution, allowing us to understand the performance of the two-stage conservative group testing algorithm of Aldridge.

We consider a queueing loss system with heterogeneous skill based servers with arbitrary distributions. We assume Poisson arrivals, with each arrival having a vector indicating which of the servers are eligible to serve it. Arrivals can only be assigned to a server that is both idle and eligible. We assume arrivals are assigned to the idle eligible server that has been idle the longest and derive, up to a multiplicative constant, the limiting distribution for this system. We show that the limiting probabilities of the ordered list of idle servers depend on the service time distributions only through their means. Moreover, conditional on the ordered list of idle servers, the remaining service times of the busy servers are independent and have their respective equilibrium service distributions. We also provide an algorithm using Gibbs sampler Markov Chain Monte Carlo method for estimating the limiting probabilities and other desired quantities of this system.

A consecutive−k−out−of−n:F system is an n−vertex graph where the system fails if and only if some k consecutive vertices all fail. Assuming that the n vertices have, independently, the respective failure probabilities q1,…, qn, the problem is to find, subject to πqi = Q (a constant), a set of q1,…, qn so as to maximize the system reliability. In the case that the graph is a circle (or cycle), Chang and Hwang [1] conjectured that q1 =… = qn, = Q1/n is optimal if n and k are relatively prime. In this paper, it is shown that the conjecture is true for sufficiently small Q. It is also shown by counterexample that the conjecture is not true in general.

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.

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.

Recently, Chen, Hsiau & Yang [1] proposed a new two-urn model with red and white balls and showed that the fractions of red balls in both urns converge almost surely to the same limit. We extend the results for the two-urn model to the q-urn model (q≥3) with similar dynamics of drawing and adding balls. We use matrix forms and martingale theory to show that the fractions of red balls in all urns converge almost surely to the same limit.