Improving the detection of unusual observations in high‐dimensional settings

Multivariate control charts are used to monitor stochastic processes for changes and unusual observations. Hotelling's T² statistic is calculated for each new observation and an out‐of‐control signal is issued if it goes beyond the control limits. However, this classical approach becomes unreliable as the number of variables p approaches the number of observations n, and impossible when p exceeds n. In this paper, we devise an improvement to the monitoring procedure in high‐dimensional settings. We regularise the covariance matrix to estimate the baseline parameter and incorporate a leave‐one‐out re‐sampling approach to estimate the empirical distribution of future observations. An extensive simulation study demonstrates that the new method outperforms the classical Hotelling T² approach in power, and maintains appropriate false positive rates. We demonstrate the utility of the method using a set of quality control samples collected to monitor a gas chromatography–mass spectrometry apparatus over a period of 67 days.     

论杀人犯罪被害人与刑事司法参与

杀人犯罪被害人是指因杀人犯罪行为而遭受直接侵害的自然人。杀人犯罪被害人参与刑事司法，具有重要意义和价值，其途径主要有量刑参与和刑事和解。此外，在被害人具有过错的情况下，其也以独特的方式参与到刑事司法程序之中。     

Teachers’ Knowledge and Beliefs About Child Sexual Abuse

Child sexual abuse is one of the main types of abuse still to be addressed within the field of education, yet the education system itself can serve as a primary tool for its prevention. A better understanding of teachers’ knowledge and beliefs about child sexual abuse will allow us to establish key starting points from which to utilize the system for prevention. Four hundred and fifty teachers participated in this study, completing a questionnaire regarding their knowledge and beliefs about child sexual abuse. The study revealed that over half the teachers, 65.3% (n = 294), had never received any type of training in child sexual abuse education and that the majority were not familiar with methods of identifying child sexual abuse, 90.7% (n = 279). Various mistaken beliefs were identified among the participating teachers, such as pathological profiles of abusers, that the vast majority of child sexual abuse implies violent behavior, and that there cannot be abusers the same age as the victim. These results indicate that knowledge deficiencies do exist about child sexual abuse among teachers and highlight the need for training in this field.     

套管的失效分析

通过收集石油套管现场失效数据资料,以套管失效为顶事件建立了石油套管的故障树,该故障树共考虑了47个不同的基本事件。通过对套管故障树的分析,得到了套管失效故障树的一阶最小割集27个,二阶最小割集29个,四阶最小割集1个,确立了套管的主要失效形式为潜在损坏、套管挤毁、套管断裂及严重腐蚀,并提出了提高套管可靠性的措施。     

FDR control of detected regions by multiscale matched filtering

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multiscale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal-to-noise ratio via matched filtering. This is achieved by considering a one-dimensional region detection problem as its equivalent zero-dimensional peak detection problem. The detection method does not require a priori knowledge of the shape of the nonzero regions. However, if the shape of the nonzero regions is known a priori, e.g., rectangular pulse, the signal regions can also be reconstructed from the detected peaks, seen as their topological point representatives. Simulations show that the method can effectively perform signal detection and reconstruction in the simulated data under high noise conditions, while controlling the FDR of detected regions and their reconstructed length.     

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。     

Retrospective change detection in categorical time series

The aim of this paper is to propose methods of detecting change in the coefficients of a multinomial logistic regression model for categorical time series offline. The alternatives to the null hypothesis of stationarity can be either the hypothesis that it is not true, or that there is a temporary change in the sequence. We use the efficient score vector of the partial likelihood function. This has several advantages. First, the alternative value of the parameter does not have to be estimated; hence, we have a procedure that has a simple structure with only one parameter estimation using all available observations. This is in contrast with the generalized likelihood ratio-based change point tests. The efficient score vector is used in various ways. As a vector, its components correspond to the different components of the multinomial logistic regression model’s parameter vector. Using its quadratic form a test can be defined, where the presence of a change in any or all parameters is tested for. If there are too many parameters one can test for any subset while treating the rest as nuisance parameters. Our motivating example is a DNA sequence of four categories, and our test result shows that in the published data the distribution of the four categories is not stationary.     

浅谈行动导向教学法在教学中的实践

主要以怠速控制阀的结构、原理及检测学习任务为例,阐述了如何在教学中实践以培养关键能力为核心的"行动导向教学法"。     

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.     

高维大数据基因网络中的社区发现——以NC方法为例

从大量基因中识别出致病基因是大数据下的一个十分重要的高维统计问题。基因间网络结构的存在使得对于致病基因的识别已从单个基因识别扩展到基因模块识别。从基因网络中挖掘出基因模块就是所谓的社区发现（或节点聚类）问题。绝大多数社区发现方法仅利用网络结构信息，而忽略节点本身的信息。Newman和Clauset于2016年提出了一个将二者有机结合的基于统计推断的社区发现方法（简称为NC方法）。本文以NC方法为案例，介绍统计方法在实际基因网络中的应用和取得的成果，并从统计学角度提出了改进措施。通过对NC方法的分析可以看出对于以基因网络为代表的非结构化数据，统计思想和原理在数据分析中仍然处于核心地位。而相应的统计方法则需要针对数据的特点及关心的问题进行相应的调整和优化。