半相依回归系统的岭型主成分改进估计

对于一类半相依回归系统，本文提出了一种岭型主成分改进估计，并讨论了这种估计及相应的两步估计的优良性质，获得了若干深入的结果。     

关于半相依回归方程组的一类两步Minimax估计的优良性

本文考虑半相依回归方程系统Y_i=X_iβ_i+ε_i,i=1,2,其中Eε_i=0,Gov(ε_i,ε_j)=σ_(ij)I_n,β_i∈R~(pi),∑=(σ_(ij))_(2×2)>0未知。在矩阵损失函数L(β,δ)=(δ-β)(δ-β)'下,我们证明了Zellner的两步估计是不可容许的,本文提出了参数β_i的一类两步Minimax估计,证明了这一类两步Minimax估计较Zellner的两步估计具有更优良的性质。     

相依误差下半参数回归模型的估计

本文在固定设计及相依误差下获得了半参数回归模型的参数估计的强相合性．     

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     

半参数回归模型的泛补偿最小二乘估计

本文首先提出泛补偿最小二乘法：接着,使用该法考虑半参数回归模型,得到了参数及非参数的估计。然后,将泛补偿最小二乘法与补偿最小二乘法进行了比较;最后用模拟的算例说明了该方法的有效性。     

协方差阵扰动模型岭估计的影响分析

本文证明了线性回归中协方差阵扰动模型岭估计的某种极限是数据删除模型的岭估计，建立了协方差阵扰动模型岭估计与Ｇ－Ｍ模型岭估计之间的关系，讨论了协方差阵扰动和数据删除对岭估计的混合影响，求得了岭估计的基于有偏估计的Ｃｏｏｋ距离。     

NA相依样本的非参数核估计

假设非参数衰退模型Yi=g(Xi)+iε,其中残差{iε}是一个具有未知公共密度函数f(x)的NA相依样本,g(x)=E(Y+X=x)是未知衰退函数.今首先在残差基础上定义了非参数估计gn(x)和^fn(x),并在适当条件下证明了gn(x)和^fn(x)分别是g(x)和f(x)的强一致相合估计,同时给出了相应的收敛速度.     

相依误差下异方差非参数回归模型的样条估计

一些经济金融等实际数据中含有非线性趋势、异方差和相依关系,固定设计和相依误差下的异方差非参数回归模型因其能够反映这些数据特征而有着重要的应用．样条方法是常用的非参数光滑方法之一．为了探究样条方法在这类模型中的可用性,本文在$\alpha$- 混合条件下,讨论了均值函数和方差函数的多项式样条估计的逐点相合性,得到了逐点收敛速度．此外,还对所讨论的方法进行了数值模拟,结果表明样条方法在这类模型的应用中是可行的．     

相依样本情形下回归函数核估计的强相合性

设(x_i,y_i)。i-1,…,n,是从取值于R~d×R′的随机向量(x,y)中抽取的平稳,φ一混合样本,设E|y|<∞,记m(x)=E(y|X=x},我们在一定的条件下证明了m(x)的如下三种核估计的强相合性,同时对m_a~(1)(x)还讨论了收敛速度。     

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。     

基于似无关模型的改进多元损失函数方法

对复杂产品或过程中存在相关性的多响应优化问题进行了研究,通过引入似无关回归模型对质量损失函数方法进行改进,将响应间的相关性综合考虑到模型拟合和参数优化两个阶段。算例表明,与传统质量损失函数方法相比,改进方法得到的最优解处期望质量损失更小。该方法克服了传统方法忽视响应间的相关性或只在优化阶段考虑相关性的不足,是对多元损失函数的重要改进。     

Response Surface Modeling and Optimization in Multiresponse Experiments Using Seemingly Unrelated Regressions

Response surface methodology (RSM) is widely used for optimizing manufacturing processes and product designs. Most applications of RSM involve several response variables. In a typical RSM study, the experimenter will build an empirical model such as the second-order model to each response and use these models to determine settings on the design variables that produce optimal or at least acceptable values for the responses. In most multiple-response RSM problems, the experimenter fits a model to each response using ordinary least squares (OLS). This article illustrates another estimation technique useful in multiple-response RSM problems, seemingly unrelated regressions (SUR). This technique can be very useful when response variables in a multiple-response RSM problem are correlated. Essentially, SUR produces more precise estimates of the model parameters than OLS when responses are correlated. This improved precision of estimation can lead to a more precise estimate of the optimum operating conditions on the process.     

协方差结构模型分析中的模型等同问题

协方差结构模型分析是近年来逐渐流行的统计分析技术。文章主要讨论了应用该技术中出现的模型等同问题。其一,何为等同模型及如何识别某个模型的备择等同模型;其二,以一个实例说明模型等同问题在研究中所造成的影响;其三,如何处理模型等同问题。     

多元线性正态SRM参数估计的渐近协方差阵及其估计

针对多元线性正态结构关系模型（SRM）,本文给出了真值向量协方差阵的广义逆构造,并在三种常见误差协方差阵假定下应用标准5法分别给出了模型结构参数约束最小二乘估计的渐近协方差阵及其相合估计。     

Optimization of Degree of Conformance in Multiresponse–Multistage Systems with a Simulation‐based Metaheuristic

In today's manufacturing and service systems, entities are progressed across the several stages of operations wherein one or more quality characteristic may be formed. The quality of final system outputs depends on the quality of intermediate characteristics as well as design parameters in each stage. This paper presents a new mathematical program to simultaneously optimize multiple quality characteristics in multiple stage systems. Multivariate form response surface methodology is applied with iterative seemingly unrelated regression as the estimation method to extract the relationships between the outputs and inputs in each stage. Because the intermediate response variables may act as covariates in the next stages, the probabilistic patterns of the response surfaces are considered by association with the quality of the previous stages. The objective function in the proposed model is the acceptance probability of the outputs based on predefined specification limits. A combination of Monte Carlo simulation and the genetic algorithm is also proposed to solve the final stochastic optimization model. At the end, the applicability of the proposed approach is illustrated by a numerical example. Copyright © 2014 John Wiley & Sons, Ltd.     

Estimation of Variance Components in Two-Stage Nested Designs With Composite Samples

If the cost of measurement is high, it is often expedient to composite several samples before taking measurements. Optimal designs for estimating (individually and jointly) the mean and variance components in two-stage nested designs are discussed, using the so-called analysis of variance estimation procedure. This traditional procedure is then compared numerically with two other unbiased estimation procedures; specific conditions are given whereby a particular procedure is superior to the other procedures.     

方差分量的非负估计

本文对含两个方差分量的线性混合模型,给出了基于谱分解估计构造方差分量的非负估计的两种新方法,由新方法构造的非负估计在一定的条件下在均方误差意义下一致优于谱分解估计,且有显式解、计算简便。     

Behavior of the Residuals of a Regression Least-Squares Model Which Is Linear in Its Parameters When the Number of Parameters Is Increased. Part 1. State of the Question. Correlation Coefficient between the Residuals

A brief critical review is given of methods and recommendations utilized when analyzing the residuals of a regression least-squares model which is linear in its parameters. The absence of the necessary rigorous solutions and the explicit contradictions present in the recommendations considerably reduce the introduction and utilization efficiency of the information contained in the residuals. A formula is proposed for calculating the correlation coefficient between the residuals together with a compact program created in the Maple V R5 mathematical software applied program package, and examples of calculations are presented.     

Estimation of the Impact of Traveler Information Apps on Urban Air Quality Improvement

With the rapid growth of vehicle population and vehicle miles traveled, automobile emission has become a severe issue in the metropolitan cities of China. There are policies that concentrate on the management of emission sources. However, improving the operation of the transportation system through apps on mobile devices, especially navigation apps, may have a unique role in promoting urban air quality. Real-time traveler information can not only help travelers avoid traffic congestion, but also advise them to adjust their departure time, mode, or route, or even to cancel trips. Will such changes in personal travel patterns have a significant impact in decreasing emissions? If so, to what extent will they impact urban air quality? The aim of this study is to determine how urban traffic emission is affected by the use of navigation apps. With this work, we attempt to answer the question of whether the real-time traffic information provided by navigation apps can help to improve urban air quality. Some of these findings may provide references for the formulation of urban traffic and environmental policies.