基于惩罚似然比的高维空间相关过程EWMA质量监控模型

变量选择控制图是高维统计过程监控的重要方法。针对传统变量选择控制图较少考虑高维过程空间相关性而造成监控效率低的问题,提出一种基于Fused-LASSO的高维空间相关过程监控模型。首先,利用Fused LASSO算法对似然比检验进行改进;然后,推导出基于惩罚似然比的监控统计量;最后,通过仿真模拟和真实案例分析所提监控模型的性能。仿真实验和真实案例均表明:在高维空间相关过程中,当相邻监控变量同时发生异常时,利用所提监控方法能够准确识别潜在异常变量,取得较好的监控效果。     

稳健的模糊回归区间分析

由于传统的回归分析易受到异常值的影响。针对输入变量为实数,输出变量和输入参数为模糊数的情况,给出了一种稳健的模糊回归区间预测模型和算法。该模型基于输出变量的隶属度函数为目标函数,以估计的区间为约束条件。给出的算法具有较强的稳健性,利用该算法估计的区间几乎不受异常值的影响。最后通过一个数值算例,与其他模型算法对比分析,验证了该模型和算法的有效性和稳健性。     

基于多变量控制图的马田系统优化研究

马田系统(MTS)是一种多元模式识别方法,它首先通过正常样本来建立基准空间,再利用正交表和信噪比来筛选有效变量,最后通过马氏距离来进行分类、诊断和预测.当建立基准空间的正常样本中掺杂少数异常点时,MTS的性能必然会受到影响.根据多变量控制图原理对建立基准空间样品的适合性进行判别,将在控制线外的样品点删除后建立新的基准空间,并通过UCI数据集进行可行性分析及分类效果比较,结果显示:经多变量控制图优化后的MTS,其性能得到显著提高.     

稀土分离联产品质量控制研究

以稀土分离企业为背景,抽取联产品特点及质量属性,绘制单一产品的指数加权移动平均控制图和联产品的多元残差T²控制图,并将两类控制图进行对比分析,分析表明和EWMA控制图相比,联产品多元残差T²控制图能降低控制图虚发警报的概率。针对多元残差T²控制图发现的异常模式,采用支持向量机模型对异常模式进行分类处理,寻找分类规则,构造PSO-SVM分类器,运用粒子群算法对SVM参数寻优,并对得出的结果进行对比分析。分析表明该分类器能提高分类正确率,模式识别可以用于诊断稀土企业引起联产品多元残差T²控制图出现异常的原因,从而提高过程质量管理水平。     

有效商Myerson值的公理化刻画

在具有图结构的合作对策中，Myerson值(Myerson, 1977)是一个著名的分配规则，它可以由分支有效性和公平性或者平衡贡献性所唯一确定。在实际中，图结构可能并不影响大联盟的形成，只是由于参与者在网络中所处的位置不同，对其讨价还价能力会产生影响。换句话说，图结构会对分配格局产生影响，但对大联盟的形成没有影响。这促使人们开始考虑Myerson值的有效推广问题。文献中已经提出了Myerson的几种有效推广形式。2020年，Li和Shan提出了有效商Myerson值并给出了公理化刻画，它是Myerson值一种新的有效推广形式。本文首先引入了准商盈余公平性这一性质，然后结合有效性和Myerson值黏性给出了有效商Myerson值的新公理化刻画。其次，通过应用案例，将该值和其他值做了比较分析。     

中药降脂灵片药效的因果分析

本文对一种新型中药降脂灵片的药效进行因果分析。实验数据样本量小且是混合变量类型,传统的统计方法难以处理,本文采用图模型的方法建立一个链图模型,直观地刻画了该药对反映机体抗氧化能力和血脂水平的4个指标的因果影响。     

结构VAR的有向非循环图模型

研究用图模型方法辨识结构向量自回归(VAR)模型,图中的结点表示不同时刻的随机变量,结点间的边表示其所表示的随机变量之间存在的因果相依关系.针对建立有向非循环图的问题,提出了一种基于回归分析的判断方法,用回归方程的回归平方和之差作为统计量,确定当前变量之间相依关系的方向.与R ea le的逐一判别法和A lessio的图搜索方法相比,文中提出的基于统计分析的方法简单易行,且可获得唯一的当前变量有向非循环图.最后以两组模拟序列为例,验证了所提出的方法是可行且有效的.     

基于杠杆值大数据集抽样的异常点诊断

本文主要研究大数据集下利用杠杆值抽样后的异常点诊断问题。首先讨论了数据删除模型中参数估计的统计性质,构造了四种异常点诊断统计量;其次,根据均值漂移模型的漂移参数的假设检验问题,构造了三种检验统计量;最后,通过模拟和实证数据分析结果得出本文的结论—异常点诊断对于基于杠杆值的大数据集抽样估计起到重要的影响作用。     

Logistic回归模型的影响分析

Logistic回归模型的影响分析是Logistic回归诊断研究中的重要内容。常用的分析方法都是轮换地删除数据点后的逐步判断,而这个判断的过程主要体现在模型的诊断图上。鉴于此,通过构造诊断统计量来有效地开发诊断图成为影响分析的核心内容,并由此能较为准确地探寻出模型的强影响点。本文通过对Logistic回归模型帽子矩阵的分解以及对轮换地删除数据点后的系数估计的相对变化量进行加权,得出Logistic回归模型诊断图使其能比传统的诊断图更准确地判断出模型的强影响点。     

Logistic回归模型的统计诊断

统计诊断的主要任务就是通过诊断统计量检测已知观测数据在用既定模型拟合时的合理性,主要是找出数据当中的异常点或强影响点。本文主要研究Logostic回归模型的诊断统计量和诊断统计图。用牛顿迭代法给出Logistic回归模型的极大似然估计值,根据扰动模型得到传统的诊断统计量,结合残差、杠杆值和系数变化三者构造新的诊断统计量,绘制新的诊断统计图,通过模拟研究说明新的诊断统计量的有效性,最后用一个实际案例说明新的诊断方法的应用并进一步验证其优越性。     

Residual Diagnostics for Covariate Effects in Spatial Point Process Models

For a spatial point process model in which the intensity depends on spatial covariates, we develop graphical diagnostics for validating the covariate effect term in the model, and for assessing whether another covariate should be added to the model. The diagnostics are point-process counterparts of the well-known partial residual plots (component-plus-residual plots) and added variable plots for generalized linear models. The new diagnostics can be derived as limits of these classical techniques under increasingly fine discretization, which leads to efficient numerical approximations. The diagnostics can also be recognized as integrals of the point process residuals, enabling us to prove asymptotic results. The diagnostics perform correctly in a simulation experiment. We demonstrate their utility in an application to geological exploration, in which a point pattern of gold deposits is modeled as a point process with intensity depending on the distance to the nearest geological fault. Online supplementary materials include technical proofs, computer code, and results of a simulation study.     

A Bayesian Sampling Approach to Regression Model Checking

A necessary step in any regression analysis is checking the fit of the model to the data. Graphical methods are often employed to allow visualization of features that the data should exhibit if the model holds. Judging whether such features are present or absent in any particular diagnostic plot can be problematic. In this article I take a Bayesian approach to aid in this task. The “unusualness” of some data with respect to a model can be assessed using the predictive distribution of the data under the model; an alternative is to use the posterior predictive distribution. Both approaches can be given a sampling interpretation that can then be used to enhance regression diagnostic plots such as marginal model plots.     

Creating a suite of macros for meta-analysis in SAS: A case study in collaboration

A series of macros that have been created to perform fixed and random effects meta-analysis in SAS^® are described as is the motivation for their creation. These macros are being made freely available on the internet for others to use. The application of the macros is illustrated using an example of trials in pre-eclampsia.     

新型混联机床动力学正问题研究

研究了新型混联机床的动力学正问题,得到了动力学正问题方程.针对两种情况进行了仿真研究,一种是刀具没有承受力和力矩的作用;一种是端铣刀进行铣削.分别给出了各个变量的时间历程图和相轨图.     

A strategy for analysing mixed and pooled exponentials

It is shown that a linear plot of the mean residual life on the failure rate characterizes the mixture of two exponentials. This plot is used to estimate the two components in the mixing distribution with the two largest mixing proportions. The EM algorithm is then used with these as initial values to obtain the MLE. Gradient plots are used to see if a higher-order fit is needed. A heuristic is given on how to use the gradient plots to identify components in the higher-order fit when this is the case. Graphs of an assignment function are then used to determine if the data are from a mixed model or simply the effect of pooling.     

Variable selection in model-based discriminant analysis

A general methodology for selecting predictors for Gaussian generative classification models is presented. The problem is regarded as a model selection problem. Three different roles for each possible predictor are considered: a variable can be a relevant classification predictor or not, and the irrelevant classification variables can be linearly dependent on a part of the relevant predictors or independent variables. This variable selection model was inspired by a previous work on variable selection in model-based clustering. A BIC-like model selection criterion is proposed. It is optimized through two embedded forward stepwise variable selection algorithms for classification and linear regression. The model identifiability and the consistency of the variable selection criterion are proved. Numerical experiments on simulated and real data sets illustrate the interest of this variable selection methodology. In particular, it is shown that this well ground variable selection model can be of great interest to improve the classification performance of the quadratic discriminant analysis in a high dimension context.     

在有交互作用下上海、伦敦期货市场铜期货价格引导关系的研究

传统的两变量引导关系模型一般仅仅考虑到自变量（包括即时与滞后因子）对因变量独立的引导作用，往往忽略了因素之间（自变量与自变量，自变量与因变量之间）交互作用对因变量产生的影响，本文提出了一种改进的引导关系模型，在传统模型的基础上添加一个交互项来刻画因素之间的交互作用对因变量所产生的影响，并对上海期货交易所和伦敦金属交易所铜期货价格之间的引导关系做了实证分析，得到一些有意义的结果，并且改进后的模型较之传统模型检验的拟合度和精确度都有一定的提高。     

Sampling Schemes for Bayesian Variable Selection in Generalized Linear Models

Bayesian approaches to prediction and the assessment of predictive uncertainty in generalized linear models are often based on averaging predictions over different models, and this requires methods for accounting for model uncertainty. When there are linear dependencies among potential predictor variables in a generalized linear model, existing Markov chain Monte Carlo algorithms for sampling from the posterior distribution on the model and parameter space in Bayesian variable selection problems may not work well. This article describes a sampling algorithm based on the Swendsen-Wang algorithm for the Ising model, and which works well when the predictors are far from orthogonality. In problems of variable selection for generalized linear models we can index different models by a binary parameter vector, where each binary variable indicates whether or not a given predictor variable is included in the model. The posterior distribution on the model is a distribution on this collection of binary strings, and by thinking of this posterior distribution as a binary spatial field we apply a sampling scheme inspired by the Swendsen-Wang algorithm for the Ising model in order to sample from the model posterior distribution. The algorithm we describe extends a similar algorithm for variable selection problems in linear models. The benefits of the algorithm are demonstrated for both real and simulated data.     

基于EM算法的混合模型中子总体个数的研究

混合模型已成为数据分析中最流行的技术之一,由于拥有数学模型,它通常比聚类分析中的传统的方法产生的结果更精确,而关键因素是混合模型中子总体个数,它决定了数据分析的最终结果。期望最大化(EM)算法常用在混合模型的参数估计,以及机器学习和聚类领域中的参数估计中,是一种从不完全数据或者是有缺失值的数据中求解参数极大似然估计的迭代算法。学者们往往采用AIC和BIC的方法来确定子总体的个数,而这两种方法在实际的应用中的效果并不稳定,甚至可能会产生错误的结果。针对此问题,本文提出了一种利用似然函数的碎石图来确定混合模型中子总体的个数的新方法。实验结果表明,本文方法确定的子总体的个数在大部分理想的情况下可以得到与AIC、BIC方法确定的聚类个数相同的结果,而在一般的实际数据中或条件不理想的状态下,碎石图方法也可以得到更可靠的结果。随后,本文将新方法在选取的黄石公园喷泉数据的参数估计中进行了实际的应用。     

Mixtures of factor analyzers: an extension with covariates

This paper examines the analysis of an extended finite mixture of factor analyzers (MFA) where both the continuous latent variable (common factor) and the categorical latent variable (component label) are assumed to be influenced by the effects of fixed observed covariates. A polytomous logistic regression model is used to link the categorical latent variable to its corresponding covariate, while a traditional linear model with normal noise is used to model the effect of the covariate on the continuous latent variable. The proposed model turns out be in various ways an extension of many existing related models, and as such offers the potential to address some of the issues not fully handled by those previous models. A detailed derivation of an EM algorithm is proposed for parameter estimation, and latent variable estimates are obtained as by-products of the overall estimation procedure.