属性集容量确定的夹挤式测度模式及其推算模型

迄今,为解决多属性偏好关联决策属性集容量判断指数复杂性难题所提出的λ模糊测度模式与k-可加模糊测度模式,以及建构在它们之上的属性集容量确定的推算模型,尚存在着适用性差的技术不足。为此,以平衡容量判断的可操作性和容量推算的准确性为视角,提出了一种新容量测度模式,即关于容量判断与推算的夹挤式测度模式,并在此基础上通过引入决策者较易判断给出的容量序信息构建了相应的容量推算模型。基于数值模拟的对比分析表明：新模式不仅在应用可行性上高于k-可加模糊测度模式,而且从容量推算的准确性上看也明显优于λ模糊测度模式和k-可加模糊测度模式,因而对实际决策具有更强的适用性。     

目标导向层次分析方法

对含有抽象属性的多属性层次结构而言,层次分析法即AHP（包括DIS-AHP、ABS-AHP、IDE-AHP和SUP-AHP四种具体方法）会因比率比较基准缺失、权重内涵模糊不清或方案评价不保序而缺乏科学理性。为发展AHP,基于摆幅置权（SW）判断模式和多属性决策属性价值公度方法,首先给出了能为层次结构抽象属性上的SW判断提供支持的规约性多属性决策属性价值公度方法,然后由此并结合多属性价值理论给出了能够克服现有层次分析法内在缺陷的目标导向层次分析方法即ToAHP。相对于AHP,ToAHP在判断模式与权重内涵、方法建构的理论基础和相关假设检验、方案评价保序与其内在数理依据上具有明显的相对科学合理性。应用分析表明：在输入信息可比的条件下,ToAHP明显优于AHP的四种分析方法之中最具可信性的SUP-AHP方法。     

京津冀节能减排政策措施的差异与协同研究

在对京津冀1981年以来节能减排政策措施进行量化处理的基础上,建立针对节能减排政策措施有效性的计量模型,分析了京津冀节能减排政策措施的演变状况,并探究京津冀节能减排政策措施对其节能减排效果影响的差异性.研究结果表明:京津冀节能减排政策颁布经历了早期各年份相对零散、缺乏连续性到新世纪以来政策颁布数量显著增多、政策总体力度逐渐增大的过程,但三地政策总效力的增加主要是由于节能减排政策颁布数量增多引起的;京津冀三地在政策的制定过程中更多的是趋于实现短期目标,政策整体缺乏系统性和权威性;人事措施、行政措施、引导措施、财税措施、金融措施等不同节能减排政策措施对京津冀节能和减排的有效性具有明显的差异;三地对不同政策措施的使用方式、使用程度方面存在明显的差异,这对京津冀协同推进节能减排的治理工作提出挑战.论文还从京津冀完善单一节能减排政策措施的使用、加强市场手段的应用及京津冀区域协同治理等方面提出了相应政策建议.     

上海生活垃圾减量化机制问题研究

开展生活垃圾减量化机制研究,使更多的垃圾作为"二次资源"进入新的产品生产循环,从而减少最终排放到环境中的垃圾数量,确保上海的可持续发展。     

基于灰粗集属性知识简约算法的海运规则发现

海运市场运营规则能够帮助海运企业找到盈利增长点,针对海运市场营运规则难于被发现的问题,将灰色系统理论和粗糙集理论进行融合,针对知识约简中分辨函数范式转化过程中的逻辑推演难题,提出了一种基于灰色粗集融合属性的改进约简算法,选取2007年、2010年、2013年、2015年典型年份作为样本年份,应用灰色系统、粗糙集和基于灰粗集知识约简算法分别进行海运市场盈运规则发现实证分析,通过比较证明了该算法的时效性,同时发掘出了当前海运市场的船型、货品、航线、运价、运力间的营运管理规则。     

Conditional variance estimation in heteroscedastic regression models

First, we propose a new method for estimating the conditional variance in heteroscedasticity regression models. For heavy tailed innovations, this method is in general more efficient than either of the local linear and local likelihood estimators. Secondly, we apply a variance reduction technique to improve the inference for the conditional variance. The proposed methods are investigated through their asymptotic distributions and numerical performances.     

A new estimation method for Weibull-type tails based on the mean excess function

Studying the right tail of a distribution, one can classify the distributions into three classes based on the extreme value index γ$\gamma$. The class γ>0$\gamma >0$ corresponds to Pareto-type or heavy tailed distributions, while γ<0$\gamma <0$ indicates that the underlying distribution has a finite endpoint. The Weibull-type distributions form an important subgroup within the Gumbel class with γ=0$\gamma =0$. The tail behaviour can then be specified using the Weibull tail index. Classical estimators of this index show severe bias. In this paper we present a new estimation approach based on the mean excess function, which exhibits improved bias and mean squared error. The asserted properties are supported by simulation experiments and asymptotic results. Illustrations with real life data sets are provided.     

Copula structure analysis

Summary.  We extend the standard approach of correlation structure analysis for dimension reduction of high dimensional statistical data. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a model for the copula. For elliptical copulas a correlation-like structure remains, but different margins and non-existence of moments are possible. After introducing the new concept and deriving some theoretical results we observe in a simulation study the performance of the estimators: the theoretical asymptotic behaviour of the statistics can be observed even for small sample sizes. Finally, we show our method at work for a financial data set and explain differences between our copula-based approach and the classical approach. Our new method yielear models also.     

Shrinkage inverse regression estimation for model-free variable selection

Summary.  The family of inverse regression estimators that was recently proposed by Cook and Ni has proven effective in dimension reduction by transforming the high dimensional predictor vector to its low dimensional projections. We propose a general shrinkage estimation strategy for the entire inverse regression estimation family that is capable of simultaneous dimension reduction and variable selection. We demonstrate that the new estimators achieve consistency in variable selection without requiring any traditional model, meanwhile retaining the root n estimation consistency of the dimension reduction basis. We also show the effectiveness of the new estimators through both simulation and real data analysis.     

Generalized correlation and kernel causality with applications in development economics

New generalized correlation measures of 2012, GMC(Y|X), use Kernel regressions to overcome the linearity of Pearson's correlation coefficients. A new matrix of generalized correlation coefficients is such that when |r*_ij| > |r*_ji|, it is more likely that the column variable X_j is what Granger called the “instantaneous cause” or what we call “kernel cause” of the row variable X_i. New partial correlations ameliorate confounding. Various examples and simulations support robustness of new causality. We include bootstrap inference, robustness checks based on the dependence between regressor and error, and on the out-of-sample forecasts. Data for 198 countries on nine development variables support growth policy over redistribution and Deaton's criticism of foreign aid. Potential applications include Big Data, since our R code is available in the online supplementary material.