实用Box-Jenkins预测模型及应用

介绍 Box-Jenkins 的实用预测模型,并提出用随机模拟法模型的参数进行估计。与通常使用的最小二乘法相比,模拟法无需考虑初始值的选取,就能保证迭代过程的收敛性。整个程序的编制使用模块化结构,使预测者无需了解太多的这方面知识,就能方便地使用。在案例的预测过程中,只需进行几次人工干预,就能输出多种分析结果。     

A NEW SHORT EXACT GEOMETRIC CONFIDENCE INTERVAL

For discrete distributions, the standard method of producing a two‐sided confidence interval generates an interval that is exact but conservative. This paper proposes a new algorithm to produce a short exact geometric confidence interval with proven properties, and compares the new interval with the standard interval.     

Joint Initial Stocking and Transshipment—Asymptotics and Bounds

A common problem faced by many firms in their supply chains can be abstracted as follows. Periodically, or at the beginning of some selling season, the firm needs to distribute finished goods to a set of stocking locations, which, in turn, supply customer demands. Over the selling season, if and when there is a supply‐demand mismatch somewhere, a re‐distribution or transshipment will be needed. Hence, there are two decisions involved: the one‐time stocking decision at the beginning of the season and the supply/transshipment decision throughout the season. Applying a stochastic dynamic programming formulation to a two‐location model with compound Poisson demand processes, we identify the optimal supply/transshipment policy and show that the optimal initial stocking quantities can be obtained via maximizing a concave function whereas the contribution of transshipment is of order square‐root‐of T. Hence, in the context of high‐volume, fast‐moving products, the initial stocking quantity decision is a much more important contributor to the overall profit. The bounds also lead to a heuristic policy, which exhibits excellent performance in our numerical study; and we further prove both the bounds and the heuristic policy are asymptotically optimal when T approaches infinity. Extension to multiple locations is also discussed.     

Evaluation of program success for programs with multiple trials in binary outcomes

A late‐stage clinical development program typically contains multiple trials. Conventionally, the program's success or failure may not be known until the completion of all trials. Nowadays, interim analyses are often used to allow evaluation for early success and/or futility for each individual study by calculating conditional power, predictive power and other indexes. It presents a good opportunity for us to estimate the probability of program success (POPS) for the entire clinical development earlier. The sponsor may abandon the program early if the estimated POPS is very low and therefore permit resource savings and reallocation to other products. We provide a method to calculate probability of success (POS) at an individual study level and also POPS for clinical programs with multiple trials in binary outcomes. Methods for calculating variation and confidence measures of POS and POPS and timing for interim analysis will be discussed and evaluated through simulations. We also illustrate our approaches on historical data retrospectively from a completed clinical program for depression. Copyright © 2015 John Wiley & Sons, Ltd.     

Construction of Simultaneous Confidence Intervals for Ratios of Means of Lognormal Distributions

For constructing simultaneous confidence intervals for ratios of means for lognormal distributions, two approaches using a two-step method of variance estimates recovery are proposed. The first approach proposes fiducial generalized confidence intervals (FGCIs) in the first step followed by the method of variance estimates recovery (MOVER) in the second step (FGCIs–MOVER). The second approach uses MOVER in the first and second steps (MOVER–MOVER). Performance of proposed approaches is compared with simultaneous fiducial generalized confidence intervals (SFGCIs). Monte Carlo simulation is used to evaluate the performance of these approaches in terms of coverage probability, average interval width, and time consumption.     

Computation of Sharp Bounds on the Expected Value of a Supermodular Function of Risks with Given Marginals

We show that the rearrangement algorithm (RA) introduced in Puccetti and Rüschendorf (2012 Puccetti, G., Rüschendorf, L. (2012). Computation of sharp bounds on the distribution of a function of dependent risks. Journal of Computational and Applied Mathematics 236(7):1833–1840.[Crossref], [Web of Science ®] , [Google Scholar]) to compute distributional bounds can be used also to compute sharp lower and upper bounds on the expected value of a supermodular function of d random variables having fixed marginal distributions. Compared to the analytical methods existing in the literature the algorithm is widely applicable, more easily obtained and gives insight into the dependence structures attaining the bounds.     

激发后进生自信心的策略

后进生最缺乏的是自信心,转化后进生的关键是激发他们的自信心。激发后进生的自信心,应该对他们的点滴成绩加以肯定,对他们的困难予以尽力帮助,对他们的情感要仔细体验,对他们的缺点应该明确指出,对他们的人格更应该加以尊重。     

提高理性认识 增强反腐信心

为了增强反腐信心,必须对腐败现象的产生与存在有一个正确的理性认识:我国现阶段出现的腐败现象与资本主义国家的腐败问题在性质上是有区别的;中国共产党领导的政权最大限度地遏制了腐败;随着改革的深化和制度的完善,腐败问题肯定会得到更有效的解决。     

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.     

A generalized improvement procedure for variance bounds for minimum variance unbiased estimator in inverse sampling

New bounds are obtained for the variance of the minimum variance unbiased estimator of p i n inverse sampling. A generalized procedure for further improving the bounds is also discussed.