Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations

Summary.  As a part of the EUREDIT project new methods to detect multivariate outliers in incomplete survey data have been developed. These methods are the first to work with sampling weights and to be able to cope with missing values. Two of these methods are presented here. The epidemic algorithm simulates the propagation of a disease through a population and uses extreme infection times to find outlying observations. Transformed rank correlations are robust estimates of the centre and the scatter of the data. They use a geometric transformation that is based on the rank correlation matrix. The estimates are used to define a Mahalanobis distance that reveals outliers. The two methods are applied to a small data set and to one of the evaluation data sets of the EUREDIT project.     

基于移动代理的新型IDS维护更新机制设计

提出了一种基于移动代理的新型分布式入侵检测系统。该系统是针对广域网环境专门设计的,数据的处理通过各节点所设置的代理来进行分布式计算,不仅能实现全网络范围内的入侵检测功能,具有良好的可移植性;而且对网络系统和主机的资源占用较低,减少了出现网络瓶颈的可能。还建立了移动代理的新型分布式入侵检测系统的体系结构和理论分析模型,并讨论了该系统的维护更新机制。     

Detection and correction of artificial shifts in climate series

Summary.  Many long instrumental climate records are available and might provide useful information in climate research. These series are usually affected by artificial shifts, due to changes in the conditions of measurement and various kinds of spurious data. A comparison with surrounding weather-stations by means of a suitable two-factor model allows us to check the reliability of the series. An adapted penalized log-likelihood procedure is used to detect an unknown number of breaks and outliers. An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference.     

CCD图象检测信号的失真校正

本文从CCD图象输入系统误差引起图象检测信号失真的原因分析出发,以降低系统装校误差精度要求和增大元器件离散性的宽容度为目标,讨论朱真校正方法的原理和数学描述。最后,介绍两种失真校正方法。     

横向塞曼稳频He－Ne激光器的研究

稳频横向塞曼Ｈｅ－Ｎｅ激光器作为一种新型光源，具有功率大、频差低、易于相位细分的特点，因而能广泛应用于各种精密测量中．本文介绍了我们研制的稳频横向塞曼激光器，实验结果表明，其激光频率稳定度达４．７×１０（－１０），再现性达１．８×１０（－８）．     

互联网环境下学术期刊防范学术不端行为技术研究

利用互联网环境，从技术的角度出发，对一稿多投、抄袭行为、剽窃行为以及捏造行为等学术不端行为的检测与防范进行研究，提出了各种操作上可行的方法。实践证明，运用这些方法，可有效地防范有学术不端行为的文章在学术期刊上发表。     

Outlier detection in high-dimensional regression model

An outlier is defined as an observation that is significantly different from the others in its dataset. In high-dimensional regression analysis, datasets often contain a portion of outliers. It is important to identify and eliminate the outliers for fitting a model to a dataset. In this paper, a novel outlier detection method is proposed for high-dimensional regression problems. The leave-one-out idea is utilized to construct a novel outlier detection measure based on distance correlation, and then an outlier detection procedure is proposed. The proposed method enjoys several advantages. First, the outlier detection measure can be simply calculated, and the detection procedure works efficiently even for high-dimensional regression data. Moreover, it can deal with a general regression, which does not require specification of a linear regression model. Finally, simulation studies show that the proposed method behaves well for detecting outliers in high-dimensional regression model and performs better than some other competing methods.     

Do individuals successfully cover up their lies? Evidence from a compliance experiment

We study how well individuals in a face-to-face situation can delude others. We exploit data from a laboratory experiment in which participants were asked to assess video-taped statements as being rather truthful or untruthful. The statements are face-to-face tax declarations. The video clips feature each subject twice making the same declaration: One time the subject is reporting honestly, and the other time willingly dishonestly. This allows us to investigate within-subject differences in perceived truthfulness. Our study provides several novel insights. We find that individuals can cover up their lies successfully. On average, a subject is perceived as more truthful if she deceives than if she reports honestly. In particular, individuals with a genuine dishonest appearance manage to increase their perceived truthfulness by up to 14 percent when lying. Moreover, our results show that a subject appears less truthful if she had previously been caught lying. Being detected as a liar previously appears to impair self-confidence and to thereby lower an individual’s ability to deceive.     

Investment communities: Behavioral attitudes and economic dynamics

Using a real-world data set encompassing the daily portfolio holdings and exposures of complex investment funds, we derive a set of quantitative attributes to capture essential behavioral features of fund managers. We find the existence and stability of three investment attitudes, namely the conservative, the reactive, and the pro-active profiles, defining communities that respond differently when facing external shocks. The conservative community has behavioral similarities that tend to decrease due to external shocks, the reactive community members greatly increase their activity level especially during turmoil phases, while delegated investors in the pro-active community are more resilient to turbulence and counterbalance the impact of the events by adjusting their portfolio exposures in advance. We show that exogenous shocks only temporarily perturb the behavioral traits of the communities which then go back to their original states once the distress is embedded.     

Mixtures of regression models with incomplete and noisy data

The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.