具有多元权值约束的鲁棒 LPM 积极投资组合

在用方差控制投资组合风险的同时,由于方差的对称性导致投资组合的收益也受到限制． 相比之下,下偏距 (lower partial moment: LPM) 由于具有只控制风险,而不限制收益的特点,在近年来倍受关注． 但在非正态假设下,LPM 无法获得良好的解析性质． 在对资产收益分布未知的假设下,通过使用最坏情形下的LPM来度量投资组合的损失,提出了具有多元权值约束的鲁棒积极投资组合问题,并获得了具有m( m=0,1,2) -阶 LPM 约束的鲁棒积极投资组合问题的解析解． 通过分析解的性质和比较问题的有效前沿,得到了许多有趣的和新颖的结果． 数值结果比较表明,鲁棒LPM模型比经典的均值－方差模型具有许多更好的性能．     

Optimal designs for response functions with a downturn

In many toxicological assays, interactions between primary and secondary effects may cause a downturn in mean responses at high doses. In this situation, the typical monotonicity assumption is invalid and may be quite misleading. Prior literature addresses the analysis of response functions with a downturn, but so far as we know, this paper initiates the study of experimental design for this situation. A growth model is combined with a death model to allow for the downturn in mean doses. Several different objective functions are studied. When the number of treatments equals the number of parameters, Fisher information is found to be independent of the model of the treatment means and on the magnitudes of the treatments. In general, A- and D_A-optimal weights for estimating adjacent mean differences are found analytically for a simple model and numerically for a biologically motivated model. Results on c-optimality are also obtained for estimating the peak dose and the EC₅₀ (the treatment with response half way between the control and the peak response on the increasing portion of the response function). Finally, when interest lies only in the increasing portion of the response function, we propose composite D-optimal designs.     

基于白化权函数分类区分度的变权灰色聚类

针对灰色聚类指标权重确定的问题,通过定义白化权函数的分类区分度来度量各指标对聚类对象的分类所作的贡献,并据此确定分类指标的权重。在此基础上,提出了变权灰色聚类方法。结果表明,该方法能够融合聚类对象的样本信息和专家的经验,有效确定不同聚类对象的各指标权重,且适用于聚类指标的量纲不同、数量级悬殊较大的情形。最后通过一个实例说明了变权灰色聚类的实用性和有效性。     

Bayesian model comparison via parallel model output

Bayesian estimation via MCMC methods opens up new possibilities in estimating complex models. However, there is still considerable debate about how selection among a set of candidate models, or averaging over closely competing models, might be undertaken. This article considers simple approaches for model averaging and choice using predictive and likelihood criteria and associated model weights on the basis of output for models that run in parallel. The operation of such procedures is illustrated with real data sets and a linear regression with simulated data where the true model is known.     

区域海洋战略性主导产业选择研究——以天津滨海新区为例

针对区域海洋产业结构相近的问题,对区域海洋战略性主导产业的选择进行了研究,界定了海洋战略性主导产业的概念与范围,分析了其演变规律,并以天津滨海新区为例,运用层次分析一熵值组合赋权法对其海洋战略性主导产业进行了选择,最终确定了以海水综合利用为核心,海洋化工和石油天然气协同发展的海洋战略性主导产业循环经济产业链,旨在推动滨海新区海洋产业结构优化和产业层次的提升,促进天津海洋经济的发展,为同类型区域海洋产业的发展提供借鉴。     

Importance of sampling weights in multilevel modeling of international large-scale assessment data

Multilevel modeling is an important tool for analyzing large-scale assessment data. However, the standard multilevel modeling will typically give biased results for such complex survey data. This bias can be eliminated by introducing design weights which must be used carefully as they can affect the results. The aim of this paper is to examine different approaches and to give recommendations concerning handling design weights in multilevel models when analyzing large-scale assessments such as TIMSS (The Trends in International Mathematics and Science Study). To achieve the goal of the paper, we examined real data from two countries and included a simulation study. The analyses in the empirical study showed that using no weights or only level 1 weights sometimes could lead to misleading conclusions. The simulation study only showed small differences in estimation of the weighted and unweighted models when informative design weights were used. The use of unscaled or not rescaled weights however caused significant differences in some parameter estimates.     

交互式多属性群决策评价方法研究

针对多属性群决策中属性指标权重的确定,将群体看作由多个被评价且参与权重决策的独立成员组成,提出一种交互式迭代算法,以均等属性权重为起点进入迭代过程,每一次迭代在当前给定的权重参数下求解含参规划模型并计算得到新的权重参数。迭代过程终止于任一群成员在相邻两次迭代的参数权重下加权综合属性值的绝对差异控制在非阿基米德无穷小量以内,此时使用的参数权重即为各属性的最优权重。实际迭代计算过程以及属性指标权重由所有群体成员共同参与和决定,故可以认为最终优化和选择结果为绝大部分成员所接受并满意。通过一个算例以及一个关于研发项目选择的实例,说明该交互式群决策评价方法的可行性和有效性。     

A Bayesian predictive inference for small area means incorporating covariates and sampling weights

The main goal in small area estimation is to use models to ‘borrow strength’ from the ensemble because the direct estimates of small area parameters are generally unreliable. However, model-based estimates from the small areas do not usually match the value of the single estimate for the large area. Benchmarking is done by applying a constraint, internally or externally, to ensure that the ‘total’ of the small areas matches the ‘grand total’. This is particularly useful because it is difficult to check model assumptions owing to the sparseness of the data. We use a Bayesian nested error regression model, which incorporates unit-level covariates and sampling weights, to develop a method to internally benchmark the finite population means of small areas. We use two examples to illustrate our method. We also perform a simulation study to further assess the properties of our method.     

区域知识资源配置结构和谐指数测度分析

通过分析知识资源配置结构和谐的内涵和构成,构建国家区域知识资源配置结构和谐指数测量的二阶段模型,以2000年至2007年我国31个省市的知识分布为研究对象,结合熵权TOPSIS和扩展的基尼系数方法,测量我国区域人力资本知识资源结构和谐指数、知识资源投入运行结构和谐指数、区域知识经济环境和谐指数和区域总体知识资源配置结构和谐指数,主要得出以下结论:人力资本知识资源结构较为和谐,指数变化幅度较小,但没有优化趋势;知识资源投入运行结构非常和谐,且愈趋优化;区域知识经济环境和谐指数相对最不和谐,区域间存在较大差异;区域总体知识资源配置结构较为和谐,但变化幅度较大,尚没有显著优化趋向。     

基于可变区间权重的中期用电量半参数预测模型

由于数据变化规律的多样性,中期电力负荷的波动有着不同于短期、长期负荷的特点。基于电力系统复杂性的研究视角,重点讨论了中期负荷预测过程中模型的不确定性、参数的时变特性以及负荷波动的周期性规律。根据中期负荷的数据特性,建立了基于非参数修匀的半参数模型,定义了函数区间的划分粒度以及模型权重的求解方法,提出了基于可变区间权重的动态预测方法,给出了基于经验模态分解和波动能量分析的噪声序列提取、检验方法。试验研究结果表明,气候因素对用电消耗的影响最大,经济因素次之;从选取的指标来看,不同时期的影响因素对于模型的解释能力是时变的;所提方法能够对电力负荷进行精确的多粒度、多维度分析,进而掌握其局部变化规律,可有效用于电力系统中期负荷预测。