Direct density estimation as an ill-posed inverse estimation problem

A possible definition of ill-posedness in statistical estimation is the lack of qualitative robustness. In this sense direct density estimation shares ill-posedness with the more obviously ill-posed indirect density estimation models, of which it is a special case. A general construction pattern for estimators is proposed, based on suitable preconditioning, that works for both direct and indirect density estimation. Special emphasis is on its application to the direct case, where in general it yields delta-sequence estimators. More specifically both kernel and series type estimators are included depending on the choice of preconditioning operator. In particular sinc and other flattop kernel estimators emerge in a natural way.     

资本结构影响因素稳健性检验

关于资本结构影响因素的理论和实证研究存在很多相互矛盾的结论,人们通常把这个问题归咎于外部因素的影响,而没有怀疑资本结构理论自身的稳健性;本研究发现相互矛盾的研究结果并非由外部因素引起,而是由于资本结构理论本身缺乏较强的稳健性。因此,传统的资本结构理论并不适用于指导我国企业的融资实践,应该从企业外部,而不是过去理论强调的“从企业内部”去寻找影响资本结构的因素。     

供应链竞争力评价指标体系研究

供应链竞争力是指实施供应链管理的核心企业整合其合作伙伴（供应商、分销商和零售商）的组织结构和业务流程,通过对顾客多变的需求作出比竞争对手更快速、更有效的反应来获取市场竞争优势的能力。通过对影响供应链竞争力的关键因素进行分析,提出了竞争力指标体系的设计原则并建立了供应链竞争力评价指标体系,给出了各指标的含义及计算方法。     

执行新准则引起的金融企业财务状况连续变动分析——基于某银行2009-2011年两种审计报告

2006年新《企业会计准则体系》发布后,金融企业会计实务发生了重大变化.以某非上市城市商业银行2009-2011年的两种不同审计报告中的财务数据为样本,对执行新准则后金融企业财务持续变动进行比较分析,较为深入地揭示了首次执行新准则的会计年度和后续年度中主要财务指标变动情况.研究结果表明,影响金融企业财务状况的因素既有金融业的特殊会计规定,又包括一般会计规定变化.总体上看,新准则符合国际趋同的形势,具有一定的稳健性,但新准则需要持续地、进一步地与金融监管政策协调.     

中国货物贸易出口量的影响因素--基于引力模型的极值边界分析

对外贸易是拉动经济增长的三驾马车之一,我国出口贸易对经济的增长作用一直非常显著,因此对贸易出口量影响因素的深入研究十分必要。本文依据国际贸易理论,对贸易“引力模型”进行扩展,引入极值边界分析方法（Extreme Bounds Analysis , EBA ）,采用2003～2012年省际区域面板数据,对我国货物贸易出口量的影响因素进行实证检验,得出的结论是各省外商直接投资、进口额、工业产值、固定资产投资、税收收入、最终消费是我国货物贸易出口量的“稳健性（Robest ）”影响因素,并据此提出促进对外贸易发展相关政策建议。     

基于数据稳健性的农用地土壤重金属空间分异研究

研究目的:科学分析区域农用地表层土壤重金属空间分异特征,为准确追溯污染源,合理安全利用土地资源提供合理的数据支撑。研究方法:基于传统统计、数据稳健性、空间变异和空间插值构建土壤重金属空间分异研究方法体系。研究结果:以江苏省某市为例,探讨6种农用地表层土壤重金属As、Cd、Cu、Hg、Pb、Zn的统计特征、数据稳健性、空间变异特征和空间分布情况。结果表明:这6种重金属原始数据均不符合正态分布,呈右偏强变异,均受到自然和人为因素的共同作用。Normal Score Transformation(NST)稳健处理后的数据能保持与原始数据几乎相同的内部变异结构,据此利用反距离加权法(IDW)所做空间插值预测效果最佳。研究结论:该方法体系通过引入数据稳健性的概念补充了常规土壤重金属空间分异研究中对局部异常值的处理思路,还为后续该类研究提供了更系统的研究思路。     

一种基于DCT的图像灰度水印算法

针对二值水印信息量小的缺陷,提出一种基于DCT的图像灰度水印算法,通过修改DCT域中低频系数值来嵌入水印,易于实现。仿真实验证明本算法能够抵御剪切、噪声和JPEG压缩失真,具有良好的不可见性和鲁棒性。     

Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models

There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed models where interest is on the variance components, we study the effects of random effects misspecification on random effects inference in linear mixed models, for which there is considerably less literature. Our findings are surprising and contrary to general belief: for point estimation, maximum likelihood estimation of the variance components under misspecification is consistent, although in finite samples, both the bias and mean squared error can be substantial. For inference, we show through theory and simulation that under misspecification, standard likelihood ratio tests of truly non‐zero variance components can suffer from severely inflated type I errors, and confidence intervals for the variance components can exhibit considerable under coverage. Furthermore, neither of these problems vanish asymptotically with increasing the number of clusters or cluster size. These results have major implications for random effects inference, especially if the true random effects distribution is heavier tailed than the normal. Fortunately, simple graphical and goodness‐of‐fit measures of the random effects predictions appear to have reasonable power at detecting misspecification. We apply linear mixed models to a survey of more than 4 000 high school students within 100 schools and analyse how mathematics achievement scores vary with student attributes and across different schools. The application demonstrates the sensitivity of mixed model inference to the true but unknown random effects distribution.     

Convex risk measures for the aggregation of multiple information sources and applications in insurance

We propose a novel class of convex risk measures, based on the concept of the Fréchet mean, designed in order to handle uncertainty which arises from multiple information sources regarding the risk factors of interest. The proposed risk measures robustly characterize the exposure of the firm, by filtering out appropriately the partial information available in individual sources into an aggregate model for the risk factors of interest. Importantly, the proposed risks can be expressed in closed analytic forms allowing for interesting qualitative interpretations as well as comparative statics and thus facilitate their use in the everyday risk management process of the insurance firms. The potential use of the proposed risk measures in insurance is illustrated by two concrete applications, capital risk allocation and premia calculation under uncertainty.     

Robustness and U.S. Monetary Policy Experimentation

We study how a concern for robustness modifies a policymaker's incentive to experiment. A policymaker has a prior over two submodels of inflation‐unemployment dynamics. One submodel implies an exploitable trade‐off, the other does not. Bayes' law gives the policymaker an incentive to experiment. The policymaker fears that both submodels and his prior probability distribution over them are misspecified. We compute decision rules that are robust to misspecifications of each submodel and of the prior distribution over submodels. We compare robust rules to ones that Cogley, Colacito, and Sargent (2007) computed assuming that the models and the prior distribution are correctly specified. We explain how the policymaker's desires to protect against misspecifications of the submodels, on the one hand, and misspecifications of the prior over them, on the other, have different effects on the decision rule.