通过成对数据的统计分析发展学生的数据分析素养

必修课程安排了样本数据的直观表示方法、样本数据的统计特征(集中趋势参数、离散程度参数)的刻画方法,并根据样本数据的统计特征估计总体的相应特征.这些方法属于单变量统计问题,其核心思想是用样本估计总体.接下来以样本估计总体为核心思想,结合典型实例,利用成对样本数据的统计相关性研究两个变量之间的统计相关性,采用的方法是先直观...     

偏最小二乘建模在R软件中的实现及实证分析

通过介绍偏最小二乘（PLS）的建模和显著性检验原理,解决了小样本多变量且变量间存在多重共线性的回归问题,建立了多变量对多变量的回归模型,并使用R软件（版本为Ri3862．15．1）实现了PLS建模;最后基于葡萄和葡萄酒理化指标数据进行了实证分析．     

基于样本协方差矩阵的多维随机数生成方法

对于概率模型未知的多维数据样本容量扩充问题,根据主成分分析原理以及多维正态分布的性质,讨论并给出了与已知多维样本数据有相同协方差结构的模拟数据生成算法,并在此基础上给出了变量的离散化处理方法。实现了在小样本数据基础上不改变变量间协方差结构的样本容量扩充,为小样本条件下的数学建模、检验和分析提供样本数据支撑。     

基于均匀设计试验利用偏最小二乘回归对边坡稳定性的分析预测

采用均匀设计试验方案对影响边坡稳定性的因素进行测量,既能节约取样开支,又能得到均匀分散且具有代表性的小样本数据.对该小样本数据结合偏最小二乘回归方法,建立了边坡稳定性系数与各影响因素的非线性回归模型.通过对模型结构、变量投影重要性指标、相对残差值及拟合值的分析发现,基于均匀设计试验利用偏最小二乘回归法可用于对边坡稳定性的分析预测.     

基于贝叶斯伪设计与组合样本的候选者数据库网络调查的推断研究

候选者数据库网络调查的推断问题是网络调查发展中迫切需要解决的问题.基于此,提出基于贝叶斯伪设计与组合样本的非概率抽样推断方法:将网络候选者数据库的调查样本与概率样本结合,根据贝叶斯定理推导出网络候选者数据库的调查样本单元的伪权数构造式,再利用两个样本数据共同估计总体均值,最后利用Bootstrap和Jackknife方法来计算总体均值估计的方差估计.研究结果表明:基于贝叶斯伪设计与组合样本的总体均值估计比使用Elliot方法估计的总体均值偏差更小,估计效果较好;方差估计方面,Bootstrap方差估计比Jackknife方差估计的效果好.     

偏最小二乘回归法在武器装备研制费用估算中的应用

参数估算法作为 LCC的一种估算方法主要应用于武器装备研制费用的估算 .由于样本数据较少 ,且变量之间的线性相关程度高 ,如果使用传统的普通最小二乘法 (OLS)估算 ,则会严重降低估计结果的有效性 .而偏最小二乘回归法 (PLS)可以克服样本点少和高线性相关性的问题 ,使估算结果更趋于真值 .以某型无人侦察机的研制费用估算和美国军用飞机发动机研制费用估算为例 ,主要研究偏最小二乘回归法 (PLS)在武器装备研制费用估算中的应用     

多指标面板数据因子分析的分层模型及应用

针对多指标面板数据的公因子提取及评价问题,提出一种充分挖掘面板数据时间序列价值的分层因子模型.模型在底层上通过对各个时点上截面数据指标变量的精炼实现对截面样本数据的评价,将截面数据压缩成只具有时间维度的样本评价值向量;模型顶层进一步实现了对由各个截面样本评价值向量形成的综合评价矩阵时间维度的精炼,并推导出面板数据因子得分公式及评价函数.最后,运用模型方法对我国大陆31个省市国有及规模以上非国有企业生产及经营状态面板数据进行了因子分析,分析结果显示了方法的合理性.分层模型实现了对面板数据各样本的指标维度与时间维度的双重提炼,弥补了现有方法的片面性与局限性.     

多源数据融合视角下非概率样本与概率样本的大量插补推断方法

随着社会的发展,概率样本无回答率越来越高,其目标变量可能存在缺失的情况.同时,大数据与网络调查的发展使得获得的样本大多数是非概率样本,如何结合这两种样本推断总体是当今时代多源数据融合领域的一个热点问题.假设存在目标变量完全缺失的概率样本和数据完整的非概率样本,提出基于非概率样本建立超总体局部多项式模型,插补概率样本缺失的目标变量,并利用插补后的概率样本估计总体,进一步证明提出估计的渐近性质.模拟和实证研究表明:与基于非概率样本的倾向得分逆加权估计相比,提出估计的绝对相对偏差,方差与均方误差更小,且与基于真实概率样本的总体估计相接近;提出总体均值估计的方差估计的绝对相对偏差与95%置信区间覆盖率也接近于基于真实概率样本的总体估计的相应指标,估计效果较好.     

基于复杂网络的时间序列单变量波动幅度研究

为研究时间序列单变量波动幅度演变规律,文章选择伦敦金下午收盘价格作为样本数据,借鉴统计物理学的方法进行研究.利用粗粒化方法建立了价格波动幅度变化模态,运用复杂网络理论对时间序列单变量波动幅度模态的统计、变化规律和演化规律进行了分析.研究结果表明,时间序列单变量波动幅度模态分布具有幂律性、群簇性和周期性,其波动幅度模态主要通过少数几种模态进行转换与演化.本研究方法不仅可以对不同类型时间序列单变量波动幅度进行研究,同时可为多变量波动幅度及其联动波动规律研究提供思路.     

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本文对两个样本数据不完全的线性模型展开讨论, 其中线性模型协变量的观测值不缺失, 响应变量的观测值随机缺失(MAR). 我们采用逆概率加权填补方法对响应变量的缺失值进行补足, 得到两个线性回归模型``完全'样本数据, 在``完全'样本数据的基础上构造了响应变量分位数差异的对数经验似然比统计量. 与以往研究结果不同的是本文在一定条件下证明了该统计量的极限分布为标准, 降低了由于权系数估计带来的误差, 进一步构造出了精度更高的分位数差异的经验似然置信区间.     

多元成分数据的对数衬度偏最小二乘通径分析模型

本文研究多元成分数据的路径关联关系的建模问题,提出多元成分数据的对数衬度PLS通径分析模型.将中心化对数比变换与PLS通径分析方法相结合建立模型,其主要优势在于:①PLS通径分析模型对数据没有严格的分布假设要求,特别适于成分数据这类分布复杂的数据建模;②成分数据中心化对数比变换后的变量完全多重相关,PLS方法能够有效解决这一问题;③PLS通径分析模型特别适于多元成分数据这类具有层次关系的数据结构的建模,通过结构模型揭示多元成分数据之间的整体性路径关联关系,通过测量模型揭示成分数据与其成分分量之间的构成关系.更重要的是,本文的方法研究遵循成分数据所特有的代数基本理论,推导出模型的成分数据对数衬度隐变量的表达形式,从理论上证明了该建模方法的科学合理性.最后,将本方法用于北京市三次产业的投资结构、GDP结构、就业结构的路径关联关系的分析中,通过实证研究验证模型的可行性和应用价值.     

Approximate Conditional Inference in Logistic and Loglinear Models

Abstract

Recently developed small-sample asymptotics provide nearly exact inference for parametric statistical models. One approach is via approximate conditional and marginal inference, respectively, in multiparameter exponential families and regression-scale models. Although the theory is well developed, these methods are under-used in practical work. This article presents a set of S-Plus routines for approximate conditional inference in logistic and loglinear regression models. It represents the first step of a project to create a library for small-sample inference which will include methods for some of the most widely used statistical models. Details of how the methods have been implemented are discussed. An example illustrates the code.     

On the convergence of the partial least squares path modeling algorithm

This paper adds to an important aspect of Partial Least Squares (PLS) path modeling, namely the convergence of the iterative PLS path modeling algorithm. Whilst conventional wisdom says that PLS always converges in practice, there is no formal proof for path models with more than two blocks of manifest variables. This paper presents six cases of non-convergence of the PLS path modeling algorithm. These cases were estimated using Mode A combined with the factorial scheme or the path weighting scheme, which are two popular options of the algorithm. As a conclusion, efforts to come to a proof of convergence under these schemes can be abandoned, and users of PLS should triangulate their estimation results.     

Reliability Estimation of Classification Algorithms. I. Introduction to the Problem. Point Frequency Estimates

The study deals with estimating the reliability of classification algorithms. The present article (the first of three) examines the statistical methods used for point estimation of the reliability of classifiers in the frequency approach. The estimation results are applicable to the small-sample case, which is of considerable importance in practice.     

基于ELM算法的羊肉水分含量的快速无损检测

在近红外光谱900-1700nm的波长范围内采集南疆羊肉的光谱数据,来研究水分含量的快速无损检测.为减弱非目标因素对光谱的影响,采用SNV和去趋势法对光谱数据进行预处理.为降低建模的复杂度,去除共线性的影响,采用连续投影算法和相关系数法相结合选取8个特征波长变量,最后使用PLS和ELM算法分别进行建模.实验表明,与采用全光谱波段建模相比,采用特征波长变量建模,PLS和ELM算法的运行时间都大大缩短,并且在运行时间和预测精度上,ELM算法均优于PLS算法.ELM算法采用8个特征波段变量建模,预测精度达到0.9768,均方误差为4.4291e-04,相关系数为0.7603,运行时间可控制在1e-04s之下,这可为研发羊肉水分含量的便携式检测装置提供理论参考.     

Reliability Estimation of Classification Algorithms. II. Integral Estimates

This article concludes the examination of reliability estimates of classification algorithms. It reviews the statistical methods used for interval estimation of the reliability of classifiers in the frequency and Bayesian approaches. “Hybrid” estimates combining both approaches are also considered. These estimates are particularly important as they are applicable to the small-sample case.__________Translated from Prikladnaya Matematika i Informatika, No. 17, pp. 112 – 128, 2004.     

Likelihood-Based Inference for Extreme Value Models

Estimation of the extremal behavior of a process is often based on the fitting of asymptotic extreme value models to relatively short series of data. Maximum likelihood has emerged as a flexible and powerful modeling tool in such applications, but its performance with small samples has been shown to be poor relative to an alternative fitting procedure based on probability weighted moments. We argue here that the small-sample superiority of the probability weighted moments estimator is due to the assumption of a restricted parameter space, corresponding to finite population moments. To incorporate similar information in a likelihood-based analysis, we propose a penalized maximum likelihood estimator that retains the modeling flexibility and large-sample optimality of the maximum likelihood estimator, but improves on its small-sample properties. The properties of the penalized likelihood estimator are verified in a simulation study, and in application to sea-level data, which also enables the procedure to be evaluated in the context of structural models for extremes.     

Reliability Estimation of Classification Algorithms. II. Point Bayesian Estimates

This is the second of three articles that deal with estimating the reliability of classification algorithms. It examines the statistical methods used for point estimation of the reliability of classifiers in the Bayesian approach. The case of unequally weighted precedents is considered. The estimation results are applicable to the small-sample case, which is of considerable importance in practice.__________Translated from Prikladnaya Matematika i Informatika, No. 16, pp. 90–104, 2004.     

Robust variable selection for finite mixture regression models

Finite mixture regression (FMR) models are frequently used in statistical modeling, often with many covariates with low significance. Variable selection techniques can be employed to identify the covariates with little influence on the response. The problem of variable selection in FMR models is studied here. Penalized likelihood-based approaches are sensitive to data contamination, and their efficiency may be significantly reduced when the model is slightly misspecified. We propose a new robust variable selection procedure for FMR models. The proposed method is based on minimum-distance techniques, which seem to have some automatic robustness to model misspecification. We show that the proposed estimator has the variable selection consistency and oracle property. The finite-sample breakdown point of the estimator is established to demonstrate its robustness. We examine small-sample and robustness properties of the estimator using a Monte Carlo study. We also analyze a real data set.     

广义偏最小二乘(PLS)估计类

本文研究了一类含有偏最小二乘（partialleastsquaresPLS）估计的估计类．给出了PLS估计的一般代数形式；讨论了含有PLS估计的广义PPLS估计类的统计性质；给出了该估计类优于最小二乘估计的条件．