创业投资与技术创新关系的实证分析

对美国1988年到2003年、我国1994年到2003年的创业投资数据和替代技术创新的专利数据,进行了单位根检验和协整性检验.单位根检验结果显示创业投资时间序列和专利时间序列都是非平稳的,是一阶单整序列;协整性检验结果显示创业投资与技术创新之间存在协整关系.     

中国CPI和PPI之间的协整分析

本文通过单位根检验、协整检验、误差修正模型和格兰杰检验这一系列计量检验方法,对我国CPI和PPI之间的关系进行了分析,结果表明:CPI和PPI之间存在长期均衡关系。     

基于结构突变的人民币如期汇率市场收益率波动分析

在对人民币即期汇率（SPOT）市场收益率波动序列分析的基础上,运用时间序列波动的结构突变检验方法对SPOT市场收益率波动序列进行了检验,结果表明,SPOT市场收益率波动存在明显的结构突变现象,原因是受我国外汇管理政策的变化和金融危机的影响。     

在不同采样情况下的替代数据检验方法研究

替代数据方法是由零假设和检验统计量两部分组成.其中检验统计量是度量时间序列某些特性的数字量,它可以检验零假设在置信度范围内是否成立.目前,基于简单算式作检验统计量的替代数据检验方法在检验非线性时间序列的非线性成份时,对采样间隔有所敏感,通常在过采样的情况下,会给出虚假的结果.本文利用不同采样情况下的混沌时间序列对替代数据检验方法进行了研究,研究表明在过采样情况下用关联维数作检验统计量可得到较好的结果,但检验所需时间很长.     

改进相空间重构方法在混沌识别中的应用研究

讨论了传统非线性时间序列相空间重构方法的特点。提出了一种改进的相空间重构方法．为了揭示非线性时间序列中的非线性相关性。采用了一种基于关联积分的统计量，并研究了不同参数对它的影响．研究了延迟时间和嵌入维数之间的关系，并采用时间窗口描述这2个参数的变化规律．同时，应用改进方法计算了混沌时间序列的重构参数。重构了混沌信号的吸引子．研究结果表明。该方法能够从时间序列有效地重构原系统的相空间，为混沌信号识别提供了新的途径．     

视频监控系统中的动目标检测新方法

在分析了现有算法在复杂背景下所存在的不足的基础上，提出了一种适用于视频监控系统的基于视频序列像素时空相关性检测的动目标检测方法．该方法首先用每一帧中像素的空间相关性检测出目标，再用序列图像中目标的时间相关性检验目标的真实性，从而最终确定是否有运动目标．试验表明，该方法能很好地检测出运动目标，并具有较强的抗干扰能力．     

排列熵算法参数的优化确定方法研究

由于排列熵算法能够有效放大时间序列的微弱变化,且计算简单、实时性好,已在信号突变检测方面显示出良好的应用前景,但是排列熵算法中嵌入维数和延迟时间等参数的确定仍依赖于经验和尝试,该问题已成为排列熵算法走向工程应用的瓶颈问题。根据排列熵算法的原理,提出了基于重构时间序列最佳相空间来确定模型参数的方法。根据相空间重构的两种观点,介绍了延迟时间与嵌入维数独立确定和联合确定两种方法的基本理论,然后利用仿真信号和滚动轴承全寿命数据对两种算法进行了检验和对比。结果表明,模型参数的独立确定方法比联合确定方法对信号的异常检测更好。     

含有变点的厚尾单位根的subsampling检验

本文研究趋势项含有变点且新息为方差无穷厚尾过程的序列单位根检验问题,通过构造DF型检验,得到了其渐近分布。为避免估计统计量渐近分布中的尾指数,构造subsampling抽样方法来确定统计量渐近分布的百分位数,同时论证了subsampling抽样方法的一致性。最后,用Monte Carlo模拟证实本文所提出统计量以及subsampling抽样方法的可行性。     

基于长序列FFT和LMS原理的调制线谱分析

从数学上证明了长序列FFT分析可实现噪声抑制,从而提高信噪比,其实质相当于时间积累。利用变步长的自适应线谱增强器进行线谱增强,可以提高信噪比。用两种方法分析了实际舰船信号的调制谱,讨论了合理选取参数的方法。实验证明,合理运用这两种方法,可以达到较好的分析效果。     

非线性和非高斯型海洋噪声

双频谱分析是从含有非线性随机信号的数据中检测并辨别非线性随机信号产生机构的统计方法。双频谱分析也可用于检验所观测的数据记录是否符合基本随机过程为高斯分布这一假设。本文应用了一种最近推出的统计方法,该方法基于若干海洋环境噪声记录的双频谱估计可检验该噪声是否为高斯型分布以及在产生所观测噪声的基本机构中是否含有非线性的证据。文中研究了取自百慕大南部大西洋、东北太平洋和印度洋3个海区的7个噪声记录。收集的这些时间序列既有当地没有航船时的环境噪声,也有以本地航船噪声为主的噪声。3个海区的环境噪声记录显示出两种情况:分析时间约为lmin时,它是线性、高斯过程;分析时间约为ls时,它是非线性和非高斯过程。而对本地船舶噪声占优势的各种时间序列,不论分析时间是短是长,检验结果都是非线性和非高斯型的。     

Statistical inference on progressive-stress accelerated life testing for the logistic exponential distribution under progressive type-II censoring

The accelerated life testing (ALT) is an efficient approach and has been used in several fields to obtain failure time data of test units in a much shorter time than testing at normal operating conditions. In this article, a progressive-stress ALT under progressive type-II censoring is considered when the lifetime of test units follows logistic exponential distribution. We assume that the scale parameter of the distribution satisfying the inverse power law. First, the maximum likelihood estimates of the model parameters and their approximate confidence intervals are obtained. Next, we obtain Bayes estimators under squared error loss function with the help of Metropolis-Hasting (MH) algorithm. We also derive highest posterior density (HPD) credible intervals of the model parameters. Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation. Finally, one data set has been analyzed for illustrative purposes.     

Reliability estimation of a multicomponent stress-strength model for unit Gompertz distribution under progressive Type II censoring

We consider the problem of estimating multicomponent stress-strength (MSS) reliability under progressive Type II censoring when stress and strength variables follow unit Gompertz distributions with common scale parameter. We estimate MSS reliability under frequentist and Bayesian approaches. Bayes estimates are obtained by using Lindley approximation and Metropolis-Hastings algorithm methods. Further, we obtain uniformly minimum variance unbiased estimates of the reliability when common scale parameter is known. Asymptotic, bootstrap confidence interval and highest posterior density credible intervals have been constructed. We perform Monte Carlo simulations to compare the performance of proposed estimates and also present a discussion. Finally, three real data sets are analyzed for illustrative purposes.     

Alternative Bayes factors: Sample size determination and discriminatory power assessment

Alternative Bayes factors are families of methods used for hypothesis testing and model selection when sensitivity to priors is a concern and also when prior information is weak or lacking. This paper deals with two related problems that arise in the practical use of these model choice criteria: sample size determination and evaluation of discriminatory power. We propose a pre-experimental approach to cope with both these issues. Specifically, extending the evidential approach of Royall (J Am Stat Assoc 95(451):760–780, 2000) and following De Santis (J Stat Plan Inference 124(1):121–144, 2004), we propose a criterion for sample size choice based on the predictive probability of observing decisive and correct evidence. The basic idea is to select the minimal sample size that guarantees a sufficiently high pre-experimental probability that an alternative Bayes factor provides strong evidence in favor of the true hypothesis. It is also argued that a predictive analysis is a natural approach to the measurement of discriminatory power of alternative Bayes factors. The necessity of measuring discrimination ability depends on the fact that alternative Bayes factors are, in general, less sensitive to prior specifications than ordinary Bayes factors and that this gain in robustness corresponds to a reduced discriminative power. Finally, implementation of the predictive approach with improper priors is discussed and possible strategies are proposed.      

Estimations of parameters in Pareto reliability model in the presence of masked data

Estimations of parameters included in the individual distributions of the life times of system components in a series system are considered in this paper based on masked system life test data. We consider a series system of two independent components each has a Pareto distributed lifetime. The maximum likelihood and Bayes estimators for the parameters and the values of the reliability of the system's components at a specific time are obtained. Symmetrical triangular prior distributions are assumed for the unknown parameters to be estimated in obtaining the Bayes estimators of these parameters. Large simulation studies are done in order: (i) explain how one can utilize the theoretical results obtained; (ii) compare the maximum likelihood and Bayes estimates obtained of the underlying parameters; and (iii) study the influence of the masking level and the sample size on the accuracy of the estimates obtained.     

雷管准爆率的几种估计方法

雷管的准爆率如何估计？本文给出矩估计法、贝叶斯估计法、区间估计法，并对各种方法加以分析比较，认为贝叶斯估计法在工程爆破中最为适用。     

雷管准爆率的几种估计方法

雷管的准爆率如何估计？本文给出矩估计法、贝叶斯估计法、区间估计法，并对各种方法加以分析比较，认为贝叶斯估计法在工程爆破中最为适用。     

Statistical inference for constant-stress accelerated life tests with dependent competing risks from Marshall-Olkin bivariate exponential distribution

This paper considers a constant-stress accelerated dependent competing risks model under Type-II censoring. The dependent structure between competing risks is modeled by a Marshall-Olkin bivariate exponential distribution, and the accelerated model is described by the power rule model. The point and interval estimation of the model parameters and the reliability function under the normal usage condition at mission time are obtained by using the maximum likelihood estimation method and the bootstrap sampling technique. Moreover, the pivotal quantities based estimation are adopted to estimate the model parameters and the generalized confidence intervals. As a comparison, we also consider the Bayes estimation and the highest posterior density credible intervals for the model parameters based on conjugate priors and importance sampling method, respectively. To illustrate the proposed methodology, a Monte Carlo simulation is used to study the performances of different estimation methods. Finally, a dataset is analyzed for illustrative purpose and a comparison with the original results is also given.     

Reliability estimations of components from masked system life data

This paper introduces estimations of reliability values for the individual components in a series system using masked system life data. In particular, we compute the maximum likelihood and Bayes estimates of component reliabilities when the system components have constant failure rates. In obtaining Bayes estimates, it is assumed that the component reliabilities are independent random variables having piecewise linear prior distributions. The model is illustrated for a two-component series. A numerical simulation study is presented to show how one can utilize the present approach to compute estimations of component reliabilities for a practical problem. Further, we investigate the comparison between the maximum likelihood and Bayes estimates, based on the respective percentage errors.     

On the overhaul effect for repairable mechanical units: a Bayes approach

This paper deals with the statistical analysis, from a Bayes viewpoint, of the failure data of repairable mechanical units subjected to minimal repairs and periodic overhauls. A proportional age reduction model is assumed to model the effect of overhauls on the reliability of the unit, whereas the failure process between two successive overhaul epochs is modeled by the power law process. Point and interval estimation of model parameters, as well as of quantities of large interest, are provided on the basis of a number of suitable prior densities, which reflect different degrees of belief on the failure/repair process. Hypothesis tests on the effectiveness of performed overhauls are developed on the basis of Bayes factor, and some guidelines to perform sensitivity analysis versus the prior information are provided. Finally, a numerical application illustrates the proposed inference and testing procedures.     

Objective Bayesian methods for one-sided testing

The one-sided testing problem can be naturally formulated as the comparison between two nonnested models. In an objective Bayesian setting, that is, when subjective prior information is not available, no general method exists either for deriving proper prior distributions on parameters or for computing Bayes factor and model posterior probabilities. The encompassing approach solves this difficulty by converting the problem into a nested model comparison for which standard methods can be applied to derive proper priors. We argue that the usual way of encompassing does not have a Bayesian justification. and propose a variant of this method that provides an objective Bayesian solution. The solution proposed here is further extended to the case where nuisance parameters are present and where the hypotheses to be tested are separated by an interval. Some illustrative examples are given for regular and non-regular sampling distributions. This paper has been supported by Ministerio de Ciencia y Tecnología under grant BEC20001-2982