煤业集团供应商选择的可拓方法

结合煤业集团的实际，提出了供应商选择的指标体系。应用可拓学的理论与方法，结合熵理论，建立了基于熵权的可拓综合评价模型。由于在该模型中采用了熵权，从而避免了低层次多因素权重确定的主观性；该模型以综合关联度作为评价准则，避免了评价中的主观性。通过将该模型在平顶山煤业集团供应商选择中进行应用，得出了其最佳的供应商。而且评价过程表明，该方法易于操作和使用。     

非线性回归模型校正和应用

本文提出了非线性回归模型的校正模型，该方法尤其适于当社会经济系统发生变化时供建立预测模型之用，从而扩大了非线性回归模型的应用范围。经实例验证，该方法的效果是显著的。     

一类灰色动态投入产出优化模型的设计及其在农业经济发展规划中的应用

本文应用灰色系统理论和方法，把动态Ｌｅｏｎｔｉｅｆ投入产出模型和线性规划结合起来，建立一种灰色动态投入产出优化模型，在山东省农业经济发展规划定量研究中，应用该模型取得比较满意的结果．     

南方集体林区贮木场原木库存随机预报模型的研究

本主要论述应用多变量CAR模型预报南方集体林区贮木场原木库存量动态变化，并用计算机模拟。结果表明该方法模型预报精度高，且应用方便。     

信息技术应用的完整模型

在Ｃ．Ａ．Ｖｏｓｓ的技术应用过程模型与Ｉ．Ｊ．Ｃｈｅｎ等人的计划模型的基础上，作者通过案例分析，提出了信息技术应用的完整模型。该模型强调技术应用中的投产后阶段，并指出技术上的成功并不必然导致战略上的成功应用，而是需要投产后的不断努力，才能取得竞争优势     

期货经纪公司保证金的一种确定方法

本文基于马尔可夫链在存储论中的应用，结合Ergodic定理，得到确定期货经纪公司保证金的Ergodic模型，即一个双目标规划问题，然后应用乘积最大化准则，将该模型转化为单目标规划问题来求解。该方法考虑期货经纪公司承担的风险和对投资者的吸引程度，为保证金的确定提供新的思路。     

孢粉数据的回归分析方法

本文以内蒙古自治区东北部地区表土孢粉分析为例，应用回归分析方法建立孢粉－气候模型，并将该模型应用于相应地区的化石孢粉组合中，以研究该地区的古气候演变．     

带有层套关系因子变量的协方差分析方法

带有层套关系因子变量的方差分析模型在实际应用中有广泛的适应性。然而，由于此类模型的特殊性，我们不能利用一些通常的统计软件对它进行模型拟合成参数估计，针对这一问题，本文提出了对该模型进行分层拟合以及由设立哑变量将此类模型转化为通常的协方差分析模型再加以解决这两种可行而方便的办法。本文还提出了与之相关的模型变量优选的方法以及该模型在教育数据分析上的应用。     

灰色规划在技术转让谈判中的应用

本文以技术转让中的价格谈判为背景，建立了一类谈判问题的灰色规划模型，给出了该模型的求解步骤的方法，并通过实例说明了有关模型的应用。     

项目投资组合的风险度及其最优决策

本文针对项目组合投资问题引入了风险度概念，并建立其风险度模型．在无零风险度项目的情况下，给出了该模型的最优项目组合投资策略并证明该策略为马氏有效．在有零风险度项目的情况下，讨论了该模型的有效前沿的结构、性质和有效性，同时还论证了该模型的有效前沿与威廉·夏普提出的资本资产定价模型的有效前沿相一致的线性关系．最后作为本模型的应用，构造了”保证还本”模型，给出了其最优项目投资组合的策略．     

Extension of multivariate regression trees to interval data. Application to electricity load profiling

Summary  Several data can be presented as interval curves where intervals reflect a within variability. In particular, this representation is well adapted for load profiles, which depict the electricity consumption of a class of customers. Electricity load profiling consists in assigning a daily load curve to a customer based on their characteristics such as energy requirement. Within the load profiling scope, this paper investigates the extension of multivariate regression trees to the case of interval dependent (or response) variables. The tree method aims at setting up simultaneously load profiles and their assignment rules based on independent variables. The extension of multivariate regression trees to interval responses is detailed and a global approach is defined. It consists in a first stage of a dimension reduction of the interval response variables. Thereafter, the extension of the tree method is applied to the first principal interval components. Outputs are the classes of the interval curves where each class is characterized both by an interval load profile (e.g. the class prototype) and an assignment rule based on the independent variables.     

基于errors-in-variables的预测模型及其应用

预测是统计学实际应用的一个主要方面,多元线性回归预测是一种很好的方法,广泛地应用在各种实际领域,但其局限性及不足也是明显的。本文以一种新的观点认识数据,即认为变量的观测里均含有误差,同时认为不应删除经慎重选择进来的解释变量。为此,本文提出了一种新的多元预测方法———多元线性EIV预测。本文还考虑了新预测模型的一个实例应用,并从相对偏差上与多元回归预测进行了比较,从而揭示了多元线性EIV预测的先进性及较好的预测精度。     

Multivariate regression estimates for finite populations

The theory on regression estimate based on one auxiliary variable has been extended to that for more than one auxiliary variable. It has been found that the multivariate regression estimate (MRE) is not unbiased in general. The form of the approximate standard error of MRE is the same as that of simple regression estimate based on one auxiliary variable with the exception that the multiple correlation coefficient replaces the total correlation coefficient in the expression. It has also been found that the precision of MRE is non-decreasing, rather usually increasing as the number of auxiliary variables correlated with the dependent variable increases, assuming sample size to be large compared to the number of auxiliary variables.     

投影寻踪模型的改进及其在城市水资源承载能力预测中的应用

在城市水资源承载能力研究中,偏最小二乘回归方法能有效地处理自变量间多重线性相关性问题,但不能较好地处理因变量与自变量间复杂的非线性问题.投影寻踪神经网络耦合模型是处理非线性问题的有力工具,而且神经网络投影寻踪耦合模型稳健性高,但不能较好地处理自变量间多重线性相关性问题.本文把这两种方法结合在一起,建立了基于偏最小二乘回归的神经网络投影寻踪耦合模型,对城市水资源承载能力进行了预测,并取得了满意效果.     

On estimation in some reduced rank extended growth curve models

The general multivariate analysis of variance model has been extensively studied in the statistical literature and successfully applied in many different fields for analyzing longitudinal data. In this article, we consider the extension of this model having two sets of regressors constituting a growth curve portion and a multivariate analysis of variance portion, respectively. Nowadays, the data collected in empirical studies have relatively complex structures though often demanding a parsimonious modeling. This can be achieved for example through imposing rank constraints on the regression coefficient matrices. The reduced rank regression structure also provides a theoretical interpretation in terms of latent variables. We derive likelihood based estimators for the mean parameters and covariance matrix in this type of models. A numerical example is provided to illustrate the obtained results.     

Normal Linear Regression Models With Recursive Graphical Markov Structure

A multivariate normal statistical model defined by the Markov properties determined by an acyclic digraph admits a recursive factorization of its likelihood function (LF) into the product of conditional LFs, each factor having the form of a classical multivariate linear regression model (≡WMANOVA model). Here these models are extended in a natural way to normal linear regression models whose LFs continue to admit such recursive factorizations, from which maximum likelihood estimators and likelihood ratio (LR) test statistics can be derived by classical linear methods. The central distribution of the LR test statistic for testing one such multivariate normal linear regression model against another is derived, and the relation of these regression models to block-recursive normal linear systems is established. It is shown how a collection of nonnested dependent normal linear regression models (≡Wseemingly unrelated regressions) can be combined into a single multivariate normal linear regression model by imposing a parsimonious set of graphical Markov (≡Wconditional independence) restrictions.     

Nonparametric estimation of the spectral measure,and associated dependence measures,for multivariate extreme values using a limiting conditional representation

The traditional approach to multivariate extreme values has been through the multivariate extreme value distribution G, characterised by its spectral measure H and associated Pickands’ dependence function A. More generally, for all asymptotically dependent variables, H determines the probability of all multivariate extreme events. When the variables are asymptotically dependent and under the assumption of unit Fréchet margins, several methods exist for the estimation of G, H and A which use variables with radial component exceeding some high threshold. For each of these characteristics, we propose new asymptotically consistent nonparametric estimators which arise from Heffernan and Tawn’s approach to multivariate extremes that conditions on variables with marginal values exceeding some high marginal threshold. The proposed estimators improve on existing estimators in three ways. First, under asymptotic dependence, they give self-consistent estimators of G, H and A; existing estimators are not self-consistent. Second, these existing estimators focus on the bivariate case, whereas our estimators extend easily to describe dependence in the multivariate case. Finally, for asymptotically independent cases, our estimators can model the level of asymptotic independence; whereas existing estimators for the spectral measure treat the variables as either being independent, or asymptotically dependent. For asymptotically dependent bivariate random variables, the new estimators are found to compare favourably with existing estimators, particularly for weak dependence. The method is illustrated with an application to finance data.     

Quadratic interval innovation diffusion models for new product sales forecasting

An appropriate sales forecasting method is vital to the success of a business firm. The logistic model and the Gompertz model are usually adopted to forecast the growth trends and the potential market volume of innovative products. All of these models rely on statistics to explain the relationships between dependent and independent variables, and use crisp parameters. However, fuzzy relationships are more appropriate for describing the relationships between dependent and independent variables; these relationships require less data than traditional models to generate reasonable estimates of parameters. Therefore, we have combined fuzzy regression with the logistic and Gompertz models to develop a quadratic-interval Gompertz model and a quadratic-interval logistic model, and we applied the models to three cases. Our practical application of the two models shows that they are appropriate tools that can reveal the best and worst possible sales volume outcomes.     

Prediction of work-related accidents according to working conditions using support vector machines

Support vector machines (SVMs), which are a kind of statistical learning methods, were applied in this research work to predict occupational accidents with success. In the first place, semi-parametric principal component analysis (SPPCA) was used in order to perform a dimensional reduction, but no satisfactory results were obtained. Next, a dimensional reduction was carried out using an innovative and intelligent computing regression algorithm known as multivariate adaptive regression splines (MARS) model with good results. The variables selected as important by the previous MARS model were taken as input variables for a SVM model. This SVM technique was able to classify, according to their working conditions, those workers that have suffered a work-related accident in the last 12 months and those that have not. SVM technique does not over-fit the experimental data and gives place to a better performance than back-propagation neural network models. Finally, the results and conclusions of this study are presented.     

Log-linear Poisson autoregression

We consider a log-linear model for time series of counts. This type of model provides a framework where both negative and positive association can be taken into account. In addition time dependent covariates are accommodated in a straightforward way. We study its probabilistic properties and maximum likelihood estimation. It is shown that a perturbed version of the process is geometrically ergodic, and, under some conditions, it approaches the non-perturbed version. In addition, it is proved that the maximum likelihood estimator of the vector of unknown parameters is asymptotically normal with a covariance matrix that can be consistently estimated. The results are based on minimal assumptions and can be extended to the case of log-linear regression with continuous exogenous variables. The theory is applied to aggregated financial transaction time series. In particular, we discover positive association between the number of transactions and the volatility process of a certain stock.