带多个变点的逐段连续线性分位数回归模型及应用

研究目标：建立具有多个变点的逐段连续线性分位数回归模型（Continuous Piecewise Linear Quantile Regression with Multiple Change Points,CPLQR）。研究方法：先通过LASSO和广义贝叶斯信息准则确定变点个数,再通过线性化技巧来估计变点的位置与回归系数。研究发现：新方法能够同时确定变点个数、估计变点位置和回归系数,而且具有较强的稳健性;应用该方法于年龄和身体质量指数之间关系,进一步证实了模型的实用性。研究创新：新方法能够处理多个变点的问题,通过LASSO和广义贝叶斯信息准则确定变点数目,避免了主观判断的弊端;借助线性化技巧,解决了目标函数在变点处不可导问题。研究价值：本文结果将为分析经济、金融、医药和生物等学科中存在结构变化的数据提供强有力的研究工具。     

G856和GSM-19T质子磁力仪观测数据日变改正处理新方法

使用G856和GSM-19T质子磁力仪在同一个测区进行磁测数据采集,由于这两种型号的质子磁力仪观测数据文件格式不一样,用其配套软件相互日变改正特别困难和繁琐,并且用其配套软件日变改正没有五点或七点滑动平均。为了解决这些问题,在Microsoft Office Excel平台编写五点或七点滑动平均和日变改正VBA程序。该程序经实验验证和一个测区20971个磁测数据生产应用验证表明VBA程序的正确性。这个VBA程序适用不同型号的质子磁力仪观测数据之间日变改正,只要把观测数据转换到Microsoft Office Excel中并插入VBA程序代码即可进行日变改正,非常实用、快捷。     

我国金融数据高频收益率波动结构突变的检测研究

本文基于Lavielle和Teyssière(2005)提出的惩罚对照函数,对我国上证综指自2005年7月1日至2010年6月30日的5分钟收益率序列,及其已实现波动进行波动结构变点检测,结果发现有两个结构变点。针对这两个结构变点,本文采用了HAR-RV-J模型对其已实现波动进行分段建模,研究不同期限的投资者对股市波动的影响作用。实证分析的结果表明结构突变发生的时间均能与相应的重大经济事件相对应,而且随着时间的推移,短期投资者对股市波动的影响逐渐加大。     

IBM首席执行官的领导力法则

领导力有四要素：领导者、追随者、任务和环境。领导力法则也应随这四点的变而变。[编者按]     

线性变截面梁弹性与塑性变形分析

变截面梁的弹塑性受力和变形特性在工程构件的设计中具有广泛的应用前景。本文以线性变截面简支梁为研究对象，采用实验方法，分析了线性变截面梁的弹塑性的加卸载过程。研究结果表明：变截面简支梁的三点弯曲力-变形曲线加载过程可分为弹性、屈服段；卸载曲线平行于弹性加载段，跨中残余位移为最大位移的44.1%；基于DIC的应变测量结果表明，各阶段水平位移等值线为曲线，表明该变截面梁的三点弯曲变形不满足平截面假设。本文的研究可为变截面梁的工程应用和教学提供参考。     

关于机理变化型变结构非线性协整的研究

变结构非线性协整是协整理论发展的必然的趋势,也是经济系统复杂多变的必然需求,文章补充了变结构非线性协整的定义,并提出了机理变化型变结构非线性协整,指出其本质问题即单位根的结构突变检验,总结了几种结构突变的单位根检验方法,讨论了变结构点的估计方法,给出了基于Chow统计量的变结构协整检验和建模方法。     

中国股市收益波动特征分析

在以往文献中发现用传统的GARCH模型估计收益率序列通常表现出波动具有长记忆性特征和较高的方差持续性，这些特征可以由方差的结构性变点造成。本文采用Chow检验对上证综指收益率序列进行了方差结构性变点的检测，证实了这些结构性变点与影响中国股市收益结构的国内外重大的经济和政治事件相符合采用GARCH模型分段建模，发现了国内、国际的重大经济和政治事件对股市的影响作用。分段建模很好地刻画了我国股票市场的发展过程，各阶段的GARCH模型表明股票市场波动逐渐减缓，市场逐步成熟。     

安康水电厂几起变压器事故分析及运行维护

文章通过分析安康电厂几起变压器事故原因，阐述了主变在运行中存在的几个事故多发点，提出我厂对主变及其附属设备改造及运行维护的对策。     

主变铁芯多点接地故障分析与处理

介绍了电力变压器铁芯接地故障类型和原因、危害及分析处理步骤。结合大化电厂3号主变铁芯多点接地故障处理过程,对变压器铁芯多点接地故障的分析判断。     

对自适应回归模型的两点讨论

本文对变参数模型中的自适应回归模型提出两点讨论。     

On the Inconsistency of the Change-Point Estimator for the NE Family

Here, we provide a simple proof of the well-known classical result that the estimator of an unknown change-point is inconsistent. The proof utilizes only the law of large numbers in place of the usual random walk theory based arguments     

A Bayesian analysis of suicide data

The identification of change points in a sequence of suicide rates is one of the fundamental aspects of Durkheim’s theory. The specification of a statistical standard suitable for this purpose is the main condition for making inferences about the causes of suicide with distinctive trends of persistency and variability just as Durkheim theorized. At present, the statistical ‘strategy’ employed by the French social scientist is too ‘rudimentary’. A hundred years later, I take the opportunity to test Durkheim’s theory through modern methodological instruments, specifically the Bayesian change-point analysis. First of all, I analyzed the same suicide data which Durkheim took into consideration. Change-point analysis corroborates the Durkheimian analysis revealing the same change-points identified by the author. Secondly, I analyzed Italian suicide rates from 1864 to 2005. The change-point analysis was very useful. Durkheim’s theory ‘works’ until 1961: suicides rates increased as industrial development increased. However, after 1961 and the economic boom, they declined, and when they began increasing again, after 1984, they did not reach the same level as before. This finding obliges us to ‘adjust’ the Durkheim’s theory giving space to Halbwach’s convergence law. Therefore, as high economic and social development levels are attained, suicide rates tend to level-off: People adapt to the stress of modernization associated to low social integration levels. Although we are more ‘egoist’, individualism does not destroy identity and the sense of life as Durkheim had maintained.     

Bootstrapping sequential change-point tests for linear regression

Bootstrap methods for sequential change-point detection procedures in linear regression models are proposed. The corresponding monitoring procedures are designed to control the overall significance level. The bootstrap critical values are updated constantly by including new observations obtained from the monitoring. The theoretical properties of these sequential bootstrap procedures are investigated, showing their asymptotic validity. Bootstrap and asymptotic methods are compared in a simulation study, showing that the studentized bootstrap tests hold the overall level better especially for small historic sample sizes while having a comparable power and run length.     

Change-point analysis through integer-valued autoregressive process with application to some COVID-19 data

In this article, we consider the problem of change-point analysis for the count time series data through an integer-valued autoregressive process of order 1 (INAR(1)) with time-varying covariates. These types of features we observe in many real-life scenarios especially in the COVID-19 data sets, where the number of active cases over time starts falling and then again increases. In order to capture those features, we use Poisson INAR(1) process with a time-varying smoothing covariate. By using such model, we can model both the components in the active cases at time-point t namely, (i) number of nonrecovery cases from the previous time-point and (ii) number of new cases at time-point t. We study some theoretical properties of the proposed model along with forecasting. Some simulation studies are performed to study the effectiveness of the proposed method. Finally, we analyze two COVID-19 data sets and compare our proposed model with another PINAR(1) process which has time-varying covariate but no change-point, to demonstrate the overall performance of our proposed model.     

Monitoring risk in a ruin model perturbed by diffusion

We construct a nonparametric sequential test for the ruin probability and a corresponding change-point test in a risk model perturbed by diffusion. Some limiting properties are derived, which extend and improve on recent results of Conti (Stat Prob Lett 72:333–343, 2005) and Jahnke (Diploma thesis, University of Cologne, 2007). It is shown that the monitoring procedures can be designed such that the tests have an asymptotic prescribed false alarm rate (size) α and power 1. Some results from a small simulation study are also presented.     

Testing stationarity of functional time series

Economic and financial data often take the form of a collection of curves observed consecutively over time. Examples include, intraday price curves, yield and term structure curves, and intraday volatility curves. Such curves can be viewed as a time series of functions. A fundamental issue that must be addressed, before an attempt is made to statistically model such data, is whether these curves, perhaps suitably transformed, form a stationary functional time series. This paper formalizes the assumption of stationarity in the context of functional time series and proposes several procedures to test the null hypothesis of stationarity. The tests are nontrivial extensions of the broadly used tests in the KPSS family. The properties of the tests under several alternatives, including change-point and I(1)$I\left(1\right)$, are studied, and new insights, present only in the functional setting are uncovered. The theory is illustrated by a small simulation study and an application to intraday price curves.     

支持向量机在航材供应模型中的研究

针对部队航材供应量预测过程中,样本采集数目较少的实际情况,采用了一种新的预测方法—支持向量机。该方法基于统计学习理论的原理,较好地解决了小样本的学习问题。并以某部队2000～2007年某项航材供应量为学习样本,建立了该项航材的供应量预测模型。计算结果表明,这种方法比传统的方法具有更少的误差和更好的预测精度。     

An exact non-iterative sampling procedure for discrete missing data problems

Many statistical problems can be formulated as discrete missing data problems (MDPs). Examples include change-point problems, capture and recapture models, sample survey with non-response, zero-inflated Poisson models, medical screening/diagnostic tests and bioassay. This paper proposes an exact non-iterative sampling algorithm to obtain independently and identically distributed (i.i.d.) samples from posterior distribution in discrete MDPs. The new algorithm is essentially a conditional sampling, thus completely avoiding problems of convergence and slow convergence in iterative algorithms such as Markov chain Monte Carlo. Different from the general inverse Bayes formulae (IBF) sampler of Tan, Tian and Ng (Statistica Sinica, 13 , 2003, 625), the implementation of the new algorithm requires neither the expectation maximization nor the sampling importance resampling algorithms. The key idea is to first utilize the sampling-wise IBF to derive the conditional distribution of the missing data given the observed data, and then to draw i.i.d. samples from the complete-data posterior distribution. We first illustrate the method with a performing example and then apply the method to contingency tables with one supplemental margin for an human immunodeficiency virus study.     

A continuous mapping theorem for the argmax-functional in the non-unique case

The Argmax-Continuous Mapping Theorem (Argmax-CMT) of K im and P ollard resp. van der Vaart and Wellner has been proved to be a very useful tool in statistics for deriving distributional convergence of M-estimators. However it only works as long as the limit process possesses an almost sure unique maximizing point. In this article we prove an extension of the Argmax-CMT where almost sure uniqueness is no longer needed. Moreover our Argmax-CMT is also valid in the function space D ( R ) equipped with L indvall's version of the Skorokhod-topology. As an example the result is applied to change-point estimators.     

一种新型量子粒子群算法

本文提出一种基于量子的连续粒子群算法(Quantum Continuous Particle Swarm Optimization-QCPSO),使用量子比特编码粒子,模拟量子粒子坍塌的随机观察方法以生成种群,运用量子旋转门来产生新的种群,引入自适应变异算子保证种群多样性。性能测试表明,对于高维优化问题,本文提出的QCPSO比经典粒子群算法(PSO)和经典量子粒子群算法(AQPSO)具有更高的精度。