Analysis of an unreliable MX/G1G2/1/repeated service queue with delayed repair under randomized vacation policy

Abstract

In this article we consider an unreliable M^X/G/1 queue with two types of general heterogeneous service and optional repeated service subject to server’s break down and delayed repair under randomized vacation policy. We assume that customer arrive to the system according to a compound Poisson process. The server provides two types of general heterogeneous service and a customer can choose either type of service before its service start. After the completion of either type of service, the customer has the further option to repeat the same type of service once again. While the server is working with any types of service or repeated service, it may breakdown at any instant. Further the concept of randomized vacation is also introduced. For this model, we first derive the joint distribution of state of the server and queue size by considering both elapsed and remaining time, which is one of the objective of this article. Next, we derive Laplace Stieltjes transform of busy period distribution. Finally, we obtain some important performance measure and reliability indices of this model.     

A uniform central limit theorem for neural network-based autoregressive processes with applications to change-point analysis

We consider an autoregressive process with a nonlinear regression function that is modelled by a feedforward neural network. First, we derive a uniform central limit theorem which is useful in the context of change-point analysis. Then, we propose a test for a change in the autoregression function which – by the uniform central limit theorem – has asymptotic power one for a large class of alternatives including local alternatives not restricted to the correctly specified model.     

The Choice of Statistic on Which to Base Tight Upper Confidence Limits

We consider the problem of finding an upper 1 –α confidence limit (α < ½) for a scalar parameter of interest θ in the presence of a nuisance parameter vector ψ when the data are discrete. Using a statistic T as a starting point, Kabaila & Lloyd (1997) define what they call the tight upper limit with respect to T . This tight upper limit possesses certain attractive properties. However, these properties provide very little guidance on the choice of T itself. The practical recommendation made by Kabaila & Lloyd (1997) is that T be an approximate upper 1 –α confidence limit for θ rather than, say, an approximately median unbiased estimator of θ. We derive a large sample approximation which provides strong theoretical support for this recommendation.     

Shape Constrained Non‐parametric Estimators of the Baseline Distribution in Cox Proportional Hazards Model

Abstract. We investigate non‐parametric estimation of a monotone baseline hazard and a decreasing baseline density within the Cox model. Two estimators of a non‐decreasing baseline hazard function are proposed. We derive the non‐parametric maximum likelihood estimator and consider a Grenander type estimator, defined as the left‐hand slope of the greatest convex minorant of the Breslow estimator. We demonstrate that the two estimators are strongly consistent and asymptotically equivalent and derive their common limit distribution at a fixed point. Both estimators of a non‐increasing baseline hazard and their asymptotic properties are obtained in a similar manner. Furthermore, we introduce a Grenander type estimator for a non‐increasing baseline density, defined as the left‐hand slope of the least concave majorant of an estimator of the baseline cumulative distribution function, derived from the Breslow estimator. We show that this estimator is strongly consistent and derive its asymptotic distribution at a fixed point.     

Testing Symmetry of the Error Distribution in Nonlinear Heteroscedastic Models

Using the empirical characteristic function, we derive a Cramér-von Mises test for symmetry of the error distribution in a class of nonlinear parametric heteroscedastic models. We study the convergence of the residual-based empirical distribution function. We establish a functional limit theorem for an empirical process of residuals, and investigate the asymptotic null distribution function of our test statistic. A simulation experiment is conducted to evaluate small-sample performances of our test.     

A Bayesian analysis of ratios of proportions in tree-structured experiments

This paper concerns the calculation of Bayes estimators of ratios of outcome proportions generated by the replication of an arbitrary tree-structured compound Bernoulli experiment under a multinomial-type sampling scheme. Here the compound Bernoulli experiment is treated as a collection of linear sequences of independent generalized Bernoulli trials having Dirichlet type 1 prior probability distributions. A method of obtaining a closed-form expression of the cumulative distribution function of the ratio of proportions – from its Meijer G-function representation – is described. Bayes point and interval estimators are directly obtained from the properties the distribution function as well as its related probability density function. In addition, the density function is used to derive the probability mass function of the predictive distribution any two associated outcome categories of the experiment – under an inverse multinomial-type sampling scheme. An illustrative numerical example concerning a Bayesian analysis of a simple tree-structured mortality model for medical patients who have suffered an acute myocardial infarction (heart attack) is also included.     

Closed-Form Approximation for the AOQL of Attributes Acceptance Sample Plans

We derive upper and lower bounds at the point at which the average outgoing quality limit (AOQL) of an attributes acceptance sampling plan is achieved. Using a simple average of these bounds to approximate the ordinate of the AOQL, we develop an accurate, closed-form approximation to the AOQL. The bounds and approximation show how the parameters of a sampling plan affect the AOQL and can be used to study the behavior of the AOQL and other measures of the plan's performance.     

The Poisson Birnbaum–Saunders model with long-term survivors

In this paper, we propose a cure rate survival model by assuming that the number of competing causes of the event of interest follows the Poisson distribution and the time to event has the Birnbaum–Saunders (BS) distribution. We define the Poisson BS distribution and provide two useful representations for its density function which facilitate to obtain some mathematical properties. Two closed-form expressions for the moments of the new distribution are given. We estimate the parameters of the model with cure rate using maximum likelihood. For different parameter settings, sample sizes and censoring percentages, several simulations are performed. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and present some ways to perform a global influence study. We analyse a real data set from the medical area.     

Outlier detection in contingency tables using decomposable graphical models

For high-dimensional data, it is a tedious task to determine anomalies such as outliers. We present a novel outlier detection method for high-dimensional contingency tables. We use the class of decomposable graphical models to model the relationship among the variables of interest, which can be depicted by an undirected graph called the interaction graph. Given an interaction graph, we derive a closed-form expression of the likelihood ratio test (LRT) statistic and an exact distribution for efficient simulation of the test statistic. An observation is declared an outlier if it deviates significantly from the approximated distribution of the test statistic under the null hypothesis. We demonstrate the use of the LRT outlier detection framework on genetic data modeled by Chow–Liu trees.     

Flexible clustering via hidden hierarchical Dirichlet priors

The Bayesian approach to inference stands out for naturally allowing borrowing information across heterogeneous populations, with different samples possibly sharing the same distribution. A popular Bayesian nonparametric model for clustering probability distributions is the nested Dirichlet process, which however has the drawback of grouping distributions in a single cluster when ties are observed across samples. With the goal of achieving a flexible and effective clustering method for both samples and observations, we investigate a nonparametric prior that arises as the composition of two different discrete random structures and derive a closed-form expression for the induced distribution of the random partition, the fundamental tool regulating the clustering behavior of the model. On the one hand, this allows to gain a deeper insight into the theoretical properties of the model and, on the other hand, it yields an MCMC algorithm for evaluating Bayesian inferences of interest. Moreover, we single out limitations of this algorithm when working with more than two populations and, consequently, devise an alternative more efficient sampling scheme, which as a by-product, allows testing homogeneity between different populations. Finally, we perform a comparison with the nested Dirichlet process and provide illustrative examples of both synthetic and real data.     

On LSE in regression model for long-range dependent random fields on spheres

We study the asymptotic behaviour of least squares estimators (LSE) in regression models for long-range dependent random fields observed on spheres. The LSE can be given as a weighted functional of long-range dependent random fields. It is known that in this scenario the limits can be non-Gaussian. We derive the limit distribution and the corresponding rate of convergence for the estimators. The results were obtained under rather general assumptions on the random fields. Simulation studies were conducted to support theoretical findings.     

Theoretical L-moments and TL-moments using combinatorial identities and finite operators

Moments have been traditionally used to characterize a probability distribution. Recently, linear moments (L-moments) and trimmed L-moments (TL-moments) are appealing alternatives to the conventional moments. This paper focuses on the computation of theoretical L-moments and TL-moments and emphasizes the use of combinatorial identities. We are able to derive new closed-form formulas of L-moments and TL-moments for continuous probability distributions. Finally, closed-form formulas for the L-moments for the exponential distribution and the uniform distribution are also obtained.     

Limit theorems for dependent Bernoulli variables with statistical inference

We consider a class of dependent Bernoulli variables where the conditional success probability is a linear combination of the last few trials and the original success probability. We obtain its limit theorems including the strong law of large numbers, weak invariance principle, and law of the iterated logarithm. We also derive some statistical inference results which make the model applicable. Simulation results are exhibited as well to show that with small sample size the convergence rate is satisfying and the proposed estimators behave well.     

A discrete analogue of the Laplace distribution

Following Kemp (J. Statist. Plann. Inference 63 (1997) 223) who defined a discrete analogue of the normal distribution, we derive a discrete version of the Laplace (double exponential) distribution. In contrast with the discrete normal case, here closed-form expressions are available for the probability density function, the distribution function, the characteristic function, the mean, and the variance. We show that this discrete distribution on integers shares many properties of the classical Laplace distribution on the real line, including unimodality, infinite divisibility, closure properties with respect to geometric compounding, and a maximum entropy property. We also discuss statistical issues of estimation under the discrete Laplace model.     

POLLING SYSTEMS WITH SIMULTANEOUS BATCH ARRIVALS

We study the delay in polling systems with simultaneous batch arrivals. Arrival epochs are generated according to a Poisson process. At any arrival epoch, batches of customers may arrive simultaneously at the different queues, according to a general joint batch-size distribution. The server visits the queues in cyclic order, the service times and the switch-over times are generally distributed, and the service disciplines are general mixtures of gated and exhaustive service. We derive closed-form expressions for the expected delay at each of the queues when the load tends to unity (under proper scalings), in a general parameter setting. The results are strikingly simple and reveal explicitly how the expected delay depends on the system parameters, and in particular, on the batch-size distributions and the simultaneity of the batch arrivals. Moreover, the results suggest simple and fast-to-evaluate approximations for the expected delay in heavily loaded polling systems. Numerical experiments demonstrate that the approximations are highly accurate in medium and heavily loaded systems.     

On Esseen type inequalities for combinatorial random sums

We derive new bounds of the remainder in a combinatorial central limit theorem with a random number of summands. Esseen type inequalities are obtained for random combinatorial sums. Various moment conditions are considered. Moments of order 2 + δ, δ ∈ (0, 1], are partial cases. A variant of a combinatorial central limit theorem is proved for random sums with indices having Poisson distributions.     

Bayesian Inference of Odds Ratios in Misclassified Binary Data with a Validation Substudy

We propose a fully Bayesian model with a non-informative prior for analyzing misclassified binary data with a validation substudy. In addition, we derive a closed-form algorithm for drawing all parameters from the posterior distribution and making statistical inference on odds ratios. Our algorithm draws each parameter from a beta distribution, avoids the specification of initial values, and does not have convergence issues. We apply the algorithm to a data set and compare the results with those obtained by other methods. Finally, the performance of our algorithm is assessed using simulation studies.     

Exact inference for exponential distribution with multiply Type-I censored data

In this paper, we focus on exact inference for exponential distribution under multiple Type-I censoring, which is a general form of Type-I censoring and represents that the units are terminated at different times. The maximum likelihood estimate of mean parameter is calculated. Further, the distribution of maximum likelihood estimate is derived and it yields an exact lower confidence limit for the mean parameter. Based on a simulation study, we conclude that the exact limit outperforms the bootstrap limit in terms of the coverage probability and average limit. Finally, a real dataset is analyzed for illustration.     

Distribution of busy period in stochastic fluid models

We consider the busy period in a stochastic fluid flow model with infinite buffer where the input and output rates are controlled by a finite homogeneous Markov process. We derive an explicit expression for the distribution of the busy period and we obtain an algorithm to compute it which exhibits nice numerical properties.

     

Some results following from conditional characteristic functions

ABSTRACT

The purpose of this article is to derive rigorously some results following from conditional characteristic functions and anticipate that they will prove to be of significant applicability. Specifically, Khintchine weak law of large numbers, Lévy central limit theorem, inversion theorem, uniqueness theorem, characterization of identical distribution, and characterization of independence are all generalized to conditional setup.