Assessing Time‐Reversibility Under Minimal Assumptions

Abstract. This article considers a simple procedure for assessing whether a weakly dependent univariate stochastic process is time‐reversible. Our approach is based on a simple index of the deviation from zero of the median of the one‐dimensional marginal law of differenced data. An attractive feature of the method is that it requires no moment assumptions. Instead of relying on Gaussian asymptotic approximations, we consider using subsampling and resampling methods to construct confidence intervals for the time‐reversibility parameter, and show that such inference procedures are asymptotically valid under a mild mixing condition. The small‐sample properties of the proposed procedures are examined by means of Monte Carlo experiments and an application to real‐world data is also presented.     

基于改进灰自助法的可靠性原始参数预测

针对电力系统可靠性评估中原始参数缺乏的问题,提出原始参数预测的改进灰自助法,它将GM(1,1)模型与自助(Bootstrap)法相结合,利用Bootstrap法产生大量再生样本,用改进灰色模型来减小误差,避免小样本特别是极小样本情况下自助法可信度不高的问题。采用改进灰自助法对算例进行仿真结果表明:在点估计、区间估计以及预测的置信度和可信度上都优于Bootstrap法以及传统的极大似然法,可信度和实用性高,解决电力系统可靠性评估解决原始数据不足的问题。     

A new MM algorithm for constrained estimation in the proportional hazards model

The constrained estimation in Cox’s model for the right-censored survival data is studied and the asymptotic properties of the constrained estimators are derived by using the Lagrangian method based on Karush–Kuhn–Tucker conditions. A novel minorization–maximization (MM) algorithm is developed for calculating the maximum likelihood estimates of the regression coefficients subject to box or linear inequality restrictions in the proportional hazards model. The first M-step of the proposed MM algorithm is to construct a surrogate function with a diagonal Hessian matrix, which can be reached by utilizing the convexity of the exponential function and the negative logarithm function. The second M-step is to maximize the surrogate function with a diagonal Hessian matrix subject to box constraints, which is equivalent to separately maximizing several one-dimensional concave functions with a lower bound and an upper bound constraint, resulting in an explicit solution via a median function. The ascent property of the proposed MM algorithm under constraints is theoretically justified. Standard error estimation is also presented via a non-parametric bootstrap approach. Simulation studies are performed to compare the estimations with and without constraints. Two real data sets are used to illustrate the proposed methods.     

Wind of change: Small-scale electricity production and distribution-grid efficiency in Sweden

In this paper, we measure the technical efficiency for local electricity distribution firms in Sweden, and in particular how small and micro-scale generation affects efficiency scores. Using a two-stage data envelopment analysis to model the technical efficiency and a double bootstrap approach to estimate the determinants of inefficiencies, we show that firms are heterogeneous in terms of inefficiency, but that a large share of small and micro-scale generation is not associated with more inefficient operations.     

Capability measure for asymmetric tolerance non-normal processes applied to speaker driver manufacturing

Process capability indices, C_p(u,v), including C_p, C_pk, C_pm, and C_pmk, have been proposed in the manufacturing industry to provide numerical measures on process potential and performance for normal processes. Earlier studies considered a class of flexible capability indices, called C_Np(u,v), for processes with non-normal distributions where the tolerances are symmetric. In this paper we consider an extension of C_Np(u,v), called C”_Np(u,v), to handle non-normal processes with asymmetric tolerances. The extension takes into account the important property of the asymmetric loss function, which is shown to be more sensitive to process shift and more accurate than C_Np(u,v) in measuring process capability, hence provides better manufacturing quality assurance. Comparisons between C_Np(u,v) and the extension C”_Np(u,v) are provided. We propose a sample percentile estimator, and apply the bootstrap method to find the lower confidence bound for testing manufacturing capability. We also develop an integrated S-PLUS program to calculate the percentile estimator and the corresponding lower confidence bound. As an illustration, the proposed approach is applied to capability testing of home-theater speaker systems.     

The np Chart with Guaranteed In‐control Average Run Lengths

We evaluate the in‐control performance of the np‐control chart with estimated parameter conditional on the Phase I sample. We then apply the bootstrap method to adjust the control chart limits to guarantee the desired in‐control average run length (ARL₀) value in the monitoring stage. The adjusted limits ensure that the ARL₀ would take a value greater than the desired value (say, B ) with a certain specified probability, that is, Pr(ARL ₀ > B ) = 1 ? ρ . The results indicate that adjusting control limits is not always necessary. We present a method to design control charts such that in control and out of control run lengths are guaranteed with pre specified probabilities. This method is an improvement of the classical statistical design approach employing constraints on in control and out of control ARL because, with this approach, there is a substantial probability that the actual run length in control may be too small. In addition, using the ARL approach may result in an actual out of control run length that is too large. Some numerical examples illustrate the efficacy of this design method. Copyright © 2016 John Wiley & Sons, Ltd.     

基于Hibernate框架的科研管理信息系统的设计与实现

从大数据和电子社会科学理念入手,研究信息检索系统关键技术,根据实际工作需要,进行需求分析,针对现有信息检索技术的不足,通过采用Spring+Hibernate+Bootstrap、Solr技术、J2EE开发编码体系结构、MVC框架、MySQL数据库等技术,设计实现一种基于Hibernate框架的科研管理信息系统。     

非线性模型对不同信息准则的适用性研究 ——以港币月度实际有效汇率数据为例

利用各种不同的信息准则对港币月度实际有效汇率1964-01～2009-02间大样本数据和2004-05～2009-02间小样本数据分别进行非线性模型估计和选择,并使用Bootstrap方法对估计出的模型向前多步预测,计算相对误差(RE)和均方根误差(RSME)值,通过Wilcoxon Signed-Rank检验来比较模型的预测精度,进而评价各个模型的拟合效果,最后得出各个信息准则在大样本和小样本数据下适用性的结论.     

基于Bootstrap技术的Web服务QoS值的置信区间估计和预测

目前对Web服务QoS（Quality of Service）的预测研究,通常预测QoS的静态值,很少预测QoS值的置信区间.本文借助非参数统计学的Bootstrap技术,提出估计Web服务QoS值置信区间的方法;然后利用与当前Web用户相似的其他Web用户调用待预测Web服务的QoS历史数据,预测当前Web用户调用待预测Web服务的QoS值的置信区间.本文估计了WSDream数据集1中每个用户调用每个Web服务的QoS值的置信区间,实验发现这些置信区间的上下限近似服从重尾分布.通过随机选择WSDream数据集1中60%到90%的用户和Web服务作为训练集,预测另外10%到40%的用户和Web服务的QoS值,实验结果表明预测的QoS置信区间与估计的QoS置信区间的平均覆盖率超过70%,最高达76%.在服务选择或服务推荐时给用户提供一个估计的或预测的QoS置信区间,可以更好地满足用户的个性化需求.