Robust Hierarchical Bayes Small Area Estimation for the Nested Error Linear Regression Model

Standard model‐based small area estimates perform poorly in presence of outliers. Sinha & Rao ( 2009 ) developed robust frequentist predictors of small area means. In this article, we present a robust Bayesian method to handle outliers in unit‐level data by extending the nested error regression model. We consider a finite mixture of normal distributions for the unit‐level error to model outliers and produce noninformative Bayes predictors of small area means. Our modelling approach generalises that of Datta & Ghosh ( 1991 ) under the normality assumption. Application of our method to a data set which is suspected to contain an outlier confirms this suspicion, correctly identifies the suspected outlier and produces robust predictors and posterior standard deviations of the small area means. Evaluation of several procedures including the M‐quantile method of Chambers & Tzavidis ( 2006 ) via simulations shows that our proposed method is as good as other procedures in terms of bias, variability and coverage probability of confidence and credible intervals when there are no outliers. In the presence of outliers, while our method and Sinha–Rao method perform similarly, they improve over the other methods. This superior performance of our procedure shows its dual (Bayes and frequentist) dominance, which should make it attractive to all practitioners, Bayesians and frequentists, of small area estimation.     

Adaptive Estimation of a Conditional Density

The authors consider the problem of estimating a conditional density by a conditional kernel density estimate when the error associated with the estimate is measured by the L₁‐norm. On the basis of the combinatorial method of Devroye and Lugosi ( 1996 ), they propose a method for selecting the bandwidths adaptively and for providing a theoretical justification of the approach. They use simulated data to illustrate the finite‐sample performance of their estimator.     

新形势下以创新为目标的工程估价理论与实践教学模式研究

在与国际趋同的工程量清单计价的新形势下,本文提出了一种新的课程体系,对课程内容进行整体优化,阐述了通过建立专业图片、视频资料库,完善工程估价课程设计和毕业设计选题库,建设工程实例资源库,对教学方法、教学手段进行改革,从理论教学和实践教学上系统地研究,并构建了一整套全新的旨在培养学生创新能力的工程估价教学模式。此教学模式的探索和实践,具有较强的现实意义和理论指导意义。     

近期越南金融危机的评估

按照国际通行标准,越南已经发生金融危机。越南金融危机事出有因,是其经济虚高增长的"矫枉过正",它对周边国家经济影响有限,但给我们提供了经验教训。只要发展基础没有改变,越南今后的经济前景仍可看好。     

Online learning of time-varying stochastic factor structure by variational sequential Bayesian factor analysis

Investment tasks include forecasting volatilities and correlations of assets and portfolios. One of the tools widely utilized is stochastic factor analysis on a set of correlated time-series (e.g. asset returns). Published time-series factor models require either sufficiently wide time windows of observed data or numeric solutions by simulations. We developed a ‘variational sequential Bayesian factor analysis’ (VSBFA) algorithm to make online learning of time-varying stochastic factor structure. The VSBFA is an analytic filter to estimate unknown factor scores, factor loadings and residual variances. The covariance matrix of the time-series predicted by the VSBFA can be decomposed into loadings-based covariance and specific variances, and the former can be expressed by ‘explanatory factors’ such as systematic components of various financial market indices. We compared the VSBFA with the most practiced factor model relying on wide data windows, the rolling PCA (principal components analysis), by applying them to 9-year daily returns of 200 simulated stocks with the ‘true’ daily data-generating model completely known, and by using them to forecast volatilities of long-only and long/short global stock portfolios with 25-year monthly returns of more than 800 stocks worldwide. Accuracy of the forecast covariance matrices is measured by a (symmetrized) Kullback–Leibler distance, and accuracy of the forecast portfolio volatilities is measured by bias statistic, log-likelihood, Q-statistic, and portfolio volatility minimization. The factor-based covariance and specific variances predicted by the best VSBFA are significantly more accurate than those by the best rolling PCA.     

浅析价值工程评估专业化的必要性及其应用——以房地产项目为例

价值工程作为提高产品价值的管理技术,其理论已经比较成熟,但在具体行业缺乏普遍的应用模式。本文以房地产项目为例,阐释了以市场效益为导向的价值工程评估专业化的意义,以及具体操作流程,为价值工程评估专业化奠定理论基础。     

国外企业社会责任与企业财务绩效关联性研究综述

企业社会责任与财务绩效的关联性问题,是20多年来国外企业管理领域研究的一个热点问题。本文对国外近30年来对社会责任与财务绩效关系方面的相关研究进行总结和评价,并指出了进一步研究的方向。     

基于BP神经网络的建筑工程估价模型研究

提出了基于BP神经网络的工程造价估测模型，以工程特征为参数，用历史数据，建立模型，并验证了BP精神网络模型在工程造价估测中的优良效果，结果显示，BP神经网络在这方面具有很好的应用前景。     

关于工程结算审核的研究

建设工程价款结算是施工阶段工程造价确定与控制的重要环节。因为工程价款结算关系着多方的利益,是反映工程建设好坏的主要指标;也是加强资金控制和计划依据,还是编制竣工决算、核定新增固定资产价值的基础。因此,建设单位必须十分重视审核工程结算。     

Estimation procedures for a family of density functions representing various life-testing models

A family of density functions is considered which contains several life-testing models as specific cases. Uniformly minimum variance unbiased estimators are obtained for the positive and negative powers of the parameter, moments and reliability function. These general results provide the estimators for the specific models.