基于逻辑回归模型的流量异常检测方法研究

网络流量作为异常检测的基本数据源,其行为特征的准确描述,是网络异常行为实时检测的重要依据.本文针对流量异常检测问题,提出了一种基于逻辑回归模型的网络流量异常检测方法.通过分析源IP、目的 IP等多个网络流量基本特征,构造了网络异常行为和正常行为的训练机,并且在此基础上采用逻辑回归建立起网络异常流量挖掘模型.利用实验室所采集的真实网络流量对所构建的模型进行检测,以验证该模型的有效性.实验结果表明本文所建立的网络模型在异常流量挖掘方面准确度高、实时性好.     

Using Logistic Regression to Evaluate New Lid Designs

New product designs from competing vendors must often be evaluated in order to make the best decision on which design to use in manufacturing. In most cases, the designs must be challenged over a range of environmental factors, process tolerances, and incoming material variations. At times, the performance characteristic may take on the form of a passing or failing response. Due to the dichotomous nature of a pass/fail outcome, ordinary regression techniques based on a normal distribution of error terms with constant variance are not appropriate. In the case of a pass/fail response, a natural distribution to consider is the binomial distribution. The authors have exploited the field of generalized linear models, specifically logistic regression, to select a new lid design for a beverage container based on the correct distributional assumptions.     

直线回归分析及其数据控制评定

文章通过对直线回归分析及其数据控制评定方法理论的介绍,并结合具体工作实例,利用EXCEL表格强大的数据处理能力,依据直线回归数据控制评定理论评判数据的准确性,从而对实验室校准数据进行有效的质量控制。     

Batch Sample Design from Databases for Logistic Regression

The prevalence of large observational databases offers potential for identifying predictive relationships among variables of interest, although observational data are generally far less informative and less reliable than experimental data. We consider the problem of selecting a subset of records from a large observational database, for the purpose of designing a small but powerful experiment involving the selected records. It is assumed that the database contains the predictor variables but is missing the response variable, and that the purpose is to fit a logistic regression model after the response is obtained via the experiment. Active learning methods, which treat a similar problem, usually select records sequentially and focus on the single objective of classification accuracy. In contrast, many emerging applications require batch sample designs and have a variety of objectives that may include classification accuracy or accuracy of the estimated parameters, the latter being more in line with the optimal design of experiments (DOE) paradigm. The aim of this paper is to explore batch sampling from databases from a DOE perspective, particularly regarding the configuration, performance, and robustness of the designs that result from the different criteria. Through extensive simulation, we show that DOE‐based batch sampling methods can substantially outperform random sampling and the entropy method that is popular in active learning. We also provide insight and guidelines for selecting appropriate design criteria and modeling assumptions. Copyright © 2016 John Wiley & Sons, Ltd.     

自变量含有类型变量的线性回归模型的树方法

给出了求解自变量含有类型变量的线性回归模型的树方法。它是一个非参数方法。讨论了修剪树对参数估计和预测的影响，给出了通过修剪树提高参数估计和预测精度的充要条件。     

Conventional Analysis with Categorical Data from a Statistically Designed Experiment

Insights are offered on the interpretation of results of analysis of designed experiments with response expressed on a nominal or ordinal scale, in terms of formulation of a cause‐and‐effect mathematical model as well as the subsequent choice of factor settings for a future desired response. As generic design of experiments software packages are based on procedures of parametric statistics, the inherent limitations peculiar to the analysis of categorical data by such software packages are illustrated by a numerical example for the benefit of non‐statisticians among quality practitioners. Copyright © 2016 John Wiley & Sons, Ltd.     

运用联合分析确定在顾客满意度调查中测量的服务属性的相对重要性

确定服务属性的相对重要性是顾客满意度测量的最重要的目标之一.当前在实践中常用的两种方法是期望值减去服务表现的"差距分析法"和基于属性测评的全面满意度评价的线性回归方法.可是,这两个方法都有缺点,因此,我们提出了一个新的方法,即基于九个假设的服务情景的联合分析.该信息被用于匹配一个满意度的响应模型作为成分服务属性的函数.进而,当前的服务水平定位于响应曲面和顾客满意度最大化方向上最陡峭上升的路线,这给予了管理者最佳的方向去安排服务改进计划.通过比较提出的方法和那些当前使用的方法,我们发现在服务改进战略进展的过程中,它对属性表现的变化更加敏感.     

删失数据下一类回归模型的参数估计

考虑一类回归模型,在右删失数据下构造了参数的最小二乘估计和加权最小二乘估计,证明了估计量具有渐近正态性。模拟结果表明加权最小二乘估计比最小二乘估计有优良的性质。     

工程机械出口行业客户关系管理数据仓库建模研究

以工程机械行业的客户关系管理系统建设为背景,围绕客户价值分析主题构建数据仓库维度模型．对客户关系管理数据仓库维度建模过程中的需求分析、高级维度模型设计、详细维度模型开发、维度模型评审和确认4个阶段进行了相应的规定描述,形成可供ETL、数据挖掘、联机在线分析等使用的数据仓库维度模型构建方案．     

纵向数据半参数回归模型估计的强相合性

本文考虑如下纵向数据半参数回归模型:yij=x'ijβ g(tij) eij。基于最小二乘法和一般的非参数权函数方法给出了模型中参数β,回归函数g(·)和误差方差σ2的估计,并在适当条件下证明了估计量的强相合性。     

基于分类回归的企业技术创新影响因素测评

构建了包括资金、研发强度、创新网络等10个因素的中小企业技术创新影响因素模型.对上海和深圳两地188家中小企业进行问卷调查,运用因子分析和分类回归分析方法,对影响中小企业技术创新水平的因素进行识别和机理分析.结果表明:资金、研发强度、技术人才等8个因素对中小企业技术创新有着显著的正效应,其中,研发强度和资金的影响最为显著;而相关政策却存在有着显著的负效应.     

A Simple Method for Regression Analysis With Censored Data

Problems requiring regression analysis of censored data arise frequently in practice. For example, in accelerated testing one wishes to relate stress and average time to failure from data including unfailed units, i.e., censored observations.

Maximum likelihood is one method for obtaining the desired estimates; in this paper, we propose an alternative approach. An initial least squares fit is obtained treating the censored values as failures. Then, based upon this initial fit, the expected failure time for each censored observation is estimated. These estimates are then used, instead of the censoring times, to obtain a revised least squares fit and new expected failure times are estimated for the censored values. These are then used in a further least squares fit. The procedure is iterated until convergence is achieved. This method is simpler to implement and explain to non-statisticians than maximum likelihood and appears to have good statistical and convergence properties.

The method is illustrated by an example, and some simulation results are described. Variations and areas for further study also are discussed.     

基于顾客满意度的产品概念设计评价研究

目的为实现产品设计过程的科学决策,对子过程产品概念设计进行顾客满意度量化评价,为决策者选择概念设计方案提供参考,在一定程度上解决产品概念设计评价活动的主观性和随意性。方法确定研究方案,搜集产品概念设计阶段方案的评价指标,运用层次分析法建立评价指标体系;构建对概念设计阶段方案的顾客满意度评价模型;运用模糊层次分析法进行指标权重即指标重要度的计算;运用模糊数学思想进行指标满意度的调查与计算,在此基础上进行概念设计方案的满意度量化评价,得到满意的决策方案。结论以台灯概念设计基于顾客满意度的评价,验证了方法的可行性,有助于为决策活动提供量化评价依据,从而更有效地进行后续设计。     

截尾数据下一类半参数回归模型的估计

讨论了一类半参数回归模型y =x′β+g(t′α) +e .假定y被随机变量T右侧截尾 ,T与y独立 ,T～G。在G已知和未知两种情况下 ,构造了α、β和g(·) 的强相合估计     

Predicting STEM Degree Outcomes Based on Eighth Grade Data and Standard Test Scores

Background Early identification of students who are potential candidates for achieving a degree in a Science, Technology, Engineering, or Mathematics (STEM) major would enable educators to offer programs designed to better enhance student interests and capabilities in those areas. Purpose (Hypothesis ) This study uses an integrated model leveraging the strengths of multiple statistical techniques to analyze the educational process from pre‐high school through college and predict which students will achieve a STEM education. Design /Method The probability of earning a STEM degree is modeled using variables available as of the eighth grade as well as standardized test scores from high school. These include demographic, attitudinal, experiential, and academic performance measures derived from the National Education Longitudinal Study of 1988 (NELS:88) dataset. The integrated model combines logistic regression, survival analysis, and receiver operating characteristics (ROC) curve analysis to predict whether an individual is likely to obtain a STEM degree. Results Predicted results of the integrated model were compared to actual outcomes and those of a separate logistic regression model. The modeling process identified a set of significant predictive variables and achieved very good predictive accuracy. The integrated model and logistic regression model performed with comparable precision. Conclusions The modeling process was adept at identifying STEM students and a large pool of other degree students that might have been capable of pursuing a STEM degree. The results suggest that it is quite feasible to identify good STEM candidates for a pro‐STEM intervention to engage their interest in STEM and support stronger quantitative skill development.     

Change‐point Detection in Phase I for Profiles with Binary Data and Random Predictors

In some applications, the quality of a process must be characterized by a profile, which describes the relationship between the response variable and explanatory variables. Moreover, for some processes, especially service processes, categorical response variables are common, making statistical process control techniques for profiles with categorical response data a must. We study Phase I analysis of profiles with binary data and random explanatory variables to identify the presence of change‐points in the reference profile dataset. The change‐point detection method based on logistic regression models is proposed. The method exploits directional shift information and integrates change‐point algorithm with the generalized likelihood ratio. A diagnostic scheme for identifying the change‐point location and the shift direction is also suggested. Numerical simulations are conducted to demonstrate the detection effectiveness and the diagnostic accuracy. A real example is used to illustrate the implementation of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

Dynamic Retrospective Regression for Functional Data

Samples of curves, or functional data, usually present phase variability in addition to amplitude variability. Existing functional regression methods do not handle phase variability in an efficient way. In this article we propose a functional regression method that incorporates phase synchronization as an intrinsic part of the model, and then attains better predictive power than ordinary linear regression in a simple and parsimonious way. The finite-sample properties of the estimators are studied by simulation. As an example of application, we analyze neuromotor data arising from a study of human lip movement. This article has supplementary materials online.     

嵌入式RFID系统识别率关键因子的分析

为提高RFID系统识别率,优化设计RFID系统,需要确定影响RFID系统识别率的关键因子.基于关键过程输入变量、关键过程输出变量的模型,提出了一种分析影响RFID系统识别率关键因子的试验设计方法,并确定了关键因子为标签与读写器平面夹角、读写器天线数量、标签与读写器距离.通过测试,分别在标签嵌入汽车轮胎未硫化和硫化两种情况下,进一步确定影响嵌入式RFID系统识别率的各关键因子.最后,应用MINITAB统计软件,采用二项logistic的回归分析方法,分析了各关键因子对识别率的贡献情况.统计分析结果有利于优化设计RFID系统,提高RFID系统识别率.     

建筑施工企业顾客满意度模型实证分析

根据建筑施工项目的特征提出一个简化的建筑施工企业顾客满意度理论模型.在收集建筑施工企业顾客样本数据的基础上,利用结构方程模型软件对理论模型作了检验.研究结果表明:顾客感知质量对顾客满意度、顾客感知价值对顾客忠诚度、顾客满意度对顾客抱怨具有显著性的影响;顾客期望对顾客抱怨、顾客满意度对顾客忠诚度有正面影响;顾客抱怨对顾客忠诚度有负面影响.研究结论具有较高的精度,模型对该建筑施工企业顾客满意度有较好的解释力.     

旋转模塑技术在箱式后勤装备研制上的应用

通过对比分析与计算,发现滚塑箱具有成本、质量、性能等方面的优势,能够满足箱式后勤装备的战术技术指标和野战条件下设备的包装防护、集装承载和携行运输的需要,提出了将旋转模塑技术广泛应用于箱式后勤装备的研制。研究了适宜箱式后勤装备制造所用原材料——共混改性聚乙烯,并介绍了旋转模塑技术及其制造工艺。