Approximating M/G/1 Waiting Time Tail Probabilities

Abstract

We propose a new approximation formula for the waiting time tail probability of the M/G/1 queue with FIFO discipline and unlimited waiting space. The aim is to address the difficulty of obtaining good estimates when the tail probability has non-exponential asymptotics. We show that the waiting time tail probability can be expressed in terms of the waiting time tail probability of a notional M/G/1 queue with truncated service time distribution plus the tail probability of an extreme order statistic. The Cramér–Lundberg approximation is applied to approximate the tail probability of the notional queue. In essence, our technique extends the applicability of the Cramér–Lundberg approximation to cases where the standard Lundberg condition does not hold. We propose a simple moment-based technique for estimating the parameters of the approximation; numerical results demonstrate that our approximation can yield very good estimates over the whole range of the argument.     

Saddlepoint-Based Bootstrap Inference for Quadratic Estimating Equations

Abstract.  We propose an easy to implement method for making small sample parametric inference about the root of an estimating equation expressible as a quadratic form in normal random variables. It is based on saddlepoint approximations to the distribution of the estimating equation whose unique root is a parameter's maximum likelihood estimator (MLE), while substituting conditional MLEs for the remaining (nuisance) parameters. Monotoncity of the estimating equation in its parameter argument enables us to relate these approximations to those for the estimator of interest. The proposed method is equivalent to a parametric bootstrap percentile approach where Monte Carlo simulation is replaced by saddlepoint approximation. It finds applications in many areas of statistics including, nonlinear regression, time series analysis, inference on ratios of regression parameters in linear models and calibration. We demonstrate the method in the context of some classical examples from nonlinear regression models and ratios of regression parameter problems. Simulation results for these show that the proposed method, apart from being generally easier to implement, yields confidence intervals with lengths and coverage probabilities that compare favourably with those obtained from several competing methods proposed in the literature over the past half-century.     

关于Gauss-Weierstrass算子线性组合的加权逼近

本文研究Gauss-Weierstrass算子的一类线性组合加Jacobi权的一致逼近问题，给出了逼近的正、逆定理和逼近阶的特征刻划。     

Smoothed Histograms for Frequency Data on Irregular Intervals

Frequency tables are often constructed on intervals of irregular width. When plotted as bar charts, the underlying true density information may be quite distorted. The majority of introductory statistics texts recommend tabulating data into intervals of equal width, but seldom caution the consequences of failing to do so. An occasional introductory text correctly emphasizes that area rather than frequency should be plotted. Nevertheless, the correctly scaled density figure is often visually less informative than one might expect, with wide bins at constant height. In many cases, the right most bin interval has no well-defined end point, making its depiction some what arbitrary. In this note, we introduce a regular histogram approximation that matches the frequencies and also minimizes a roughness criterion for visual and exploratory appeal. The resulting estimate can reveal the density structure much more clearly. We also formulate an alternative criterion that explicitly takes account of the uncertainty in the bin frequencies.     

多元Ismail—May算子的一致逼近

主要给出了多元Ismail—May算子在一致逼近意义下的正、逆定理及特征刻划。     

SADDLEPOINT METHODS FOR BOOTSTRAP CONFIDENCE BANDS IN NONPARAMETRIC REGRESSION

Bootstrap techniques have been used to construct confidence bands in nonparametric regression problems (Härdle & Bowman, 1988). Yet the required simulation is generally computationally intensive and therefore makes it difficult to conduct further investigations. In this paper, two saddlepoint methods are considered as alternatives to the naive simulation procedure. Some improvements to Härdle & Bowman's bootstrap method are suggested. The improvements are numerically verified using these efficient and accurate analytic methods.     

LDPC编码的最优化HARQ方案研究

使用高斯近似和最优化理论的方法,对基于LDPC码度分布的混合自动重发请求(HARQ)方案的性能进行了研究。研究表明,根据LDPC编码的度分布和初始信道条件,能够确定一个编码码字中各个度数的比特所对应的最优的重发比例,如果按照该比例进行重发,重发功率的效率能够最大化。计算结果显示基于LDPC码度分布的混合自动重发请求方案具有最优的重发比例,是一种最优化方案。     

用逐次逼近法求解北半球斜上抛体问题

运用逐次逼近理论,给出了在北半球斜上抛体的三次近似解.     

奥尔里奇空间L_φ(μ,X)内的最佳逼近问题

设X是一实巴拿赫空间，（Ω，μ）是[O，1]上的勒贝格测度空间，φ是定义在[0，+∞）上具φ（O）＝0的严格增加的连续凸函数。L_φ（μ，X）={可测函数f:Ω→X；存在c＞0使得∫f（t）||)dμ（t）＜+∞｝。本文的主要结果之一为：若Y是X的闭子空间，则L_φ（μ，Y）是L_φ（μ，X）的存在性集充要条件为L'（μ,Y）是L'(μ，X)的存在性集；同时也给出了有关L_φ(μ，X）子空间存在性集的其他结果。